KR20210067160A - Method for game data processing - Google Patents

Abstract

A computer program stored in a computer readable recording medium performs following operation for performing game data processing when the computer program is executed in or more processors. Provided is the computer program stored in the computer readable recording medium, wherein the operation comprises: operation of generating a transaction network based on game log data; operation of dividing the transaction network into a plurality of sub-transaction networks based on a predetermined reference; and operation of determining a node included in at least one among the plurality of the sub-transaction networks as a work site account.

Description

How to process game data

The present disclosure relates to game operation, and more particularly, to a method for processing game data.

Recently, along with the rapid development of IT technology, the game industry is also rapidly growing. As the game industry grows, game companies that provide game services are also increasing explosively.

In addition, as the number of people who enjoy games increases due to the development of electronic communication technology, various types of games such as strategy simulation games and role-playing games have been developed. These games can be played both online and offline, and in particular, online games do not have time and space constraints, so many users can easily gather and enjoy the game together.

In general, players operate a character in an online game to proceed with the game, and in the course of playing the game, they acquire items or pay in-game goods to purchase items in the game from other players or NPCs (Non Player Characters).

Items and in-game goods commonly used in the game may not only facilitate the progress of an online game, but may also be a factor that increases the interest of the game. Players also trade in-game items of scarce value or in-game goods for cash.

On the other hand, as cash transactions of these items or game goods are frequently made, bot programs that automatically collect items or game goods are increasing.

In line with this trend, recently, a workshop has appeared in which a number of game servers are provided and a bot program is collectively operated to collect a large amount of items or game goods and sell them to general players to make a profit.

Therefore, there may be a demand in the industry for a method of detecting a workplace and applying a sanction thereto in operating a game.

Korean Patent Registration No. 10-1211832 discloses a system and method for detecting abnormal characters in an online game using a clustering technique.

The present disclosure has been devised in response to the above background art, and is intended to provide a method for processing game data.

According to an embodiment of the present disclosure for realizing the above-described problems, a computer program including instructions stored in a computer-readable storage medium to cause the computer to perform the following operations is disclosed. The operations may include: generating a trading network based on game log data; dividing the transaction network into a plurality of sub-transaction networks based on a predetermined criterion; and determining a node included in at least one of the plurality of sub-transaction networks as the workplace account.

In an alternative embodiment, the transaction network may include at least one of a node representing a user account or a trunk line including transaction information between users.

In an alternative embodiment, the transaction information may include at least one of a movement direction of goods or a transaction weight as quantitative information on a transaction generated between users.

In an alternative embodiment, the transaction weight is a measure indicating the degree of disadvantage in a transaction that the user has in a relationship with a counterparty, and may be determined based on a difference in transaction value between a transaction between the user and the counterparty and another user transaction.

In an alternative embodiment, the predetermined criterion is a criterion for dividing the transaction network into a plurality of sub-trading networks based on the transaction weight, and may include at least one of a first threshold value or a second threshold value for the transaction weight. .

In an alternative embodiment, the operation of dividing the trading network into a plurality of sub-transaction networks based on a predetermined criterion includes a first sub-transaction network in which a transaction weight is greater than or equal to a first threshold value, a first sub-transaction network in which a transaction weight is less than or equal to the first threshold, and a second and dividing the second sub-transaction network into a second sub-transaction network equal to or greater than the threshold value and a third sub-transaction network in which the transaction weight is less than the second threshold value.

In an alternative embodiment, the operation of determining a node included in at least one of the plurality of sub-transaction networks as the workplace account may include calculating a dominance score for each node included in the transaction network.

In an alternative embodiment, the domination score may include a score determined based on a quantitative amount of in-game goods flowing out of the node.

In an alternative embodiment, the dominance score is for selecting a first transaction weight corresponding to an edge connected to a node, a second transaction weight corresponding to an edge connected to a node adjacent to the node, or a second transaction weight reflected in the dominance score. It may include a score determined based on at least one of the selection coefficients.

In an alternative embodiment, the operation of determining the node included in at least one of the plurality of sub-transaction networks as the workplace account is a first node in which the rank of the node included in the first sub-transaction network is equal to or greater than the predetermined first rank. and determining, wherein the rank of the node includes at least one of a sum of transaction weights corresponding to at least one edge flowing into the node or a sum of transaction weights corresponding to at least one edge flowing out from the node. can

In an alternative embodiment, the method may further include performing first filtering to determine a second node by re-selecting at least some of the first nodes based on the determined number of nodes transacting with the node.

In an alternative embodiment, the operation of determining a node included in at least one of the plurality of sub-transaction networks as the workplace account determines a third node in which the rank of the node included in the third sub-transaction network is a predetermined second rank or higher It can include actions.

In an alternative embodiment, the method may further include performing second filtering to determine a fourth node by reselecting at least some of the third nodes based on the determined number of nodes transacting with the node.

In an alternative embodiment, the operation of determining a node included in at least one of the plurality of sub-transaction networks as a workplace account may include determining the node as a workplace account when the node transacted with the first node and the third node are the same node. It may include at least one of the following operation or determining the node as a workplace account when the node that has transacted with the filtered second node and the filtered fourth node are the same node.

According to another embodiment of the present disclosure, a method for processing game data includes generating a transaction network based on game log data; dividing the trading network into a plurality of sub-transaction networks based on a predetermined criterion; It may include determining a node included in at least one of the plurality of sub-transaction networks as the workplace account.

A server is disclosed according to another embodiment of the present disclosure. The server may include one or more processors; and a memory storing instructions executable by the processor; wherein the one or more processors generate a trading network based on the game log data, divide the trading network into a plurality of sub-trading networks based on a predetermined criterion, and include in at least one of the plurality of sub-trading networks. It is possible to determine which node is a workshop account.

According to an embodiment of the present disclosure, a method for processing game data may be provided.

BRIEF DESCRIPTION OF THE DRAWINGS So that the above-mentioned features of the present disclosure may be understood in detail, with a more specific description, with reference to the following embodiments, some of the embodiments are shown in the accompanying drawings. Also, like reference numerals in the drawings are intended to refer to the same or similar functions throughout the various aspects. However, it should be noted that the accompanying drawings only show certain typical embodiments of the present disclosure and are not to be considered as limiting the scope of the present disclosure, and other embodiments having the same effect may be fully appreciated. Take note.
1 is a block diagram of a server for processing game data according to an embodiment of the present disclosure.
2 is an exemplary diagram for explaining a method of detecting a workplace account according to an embodiment of the present disclosure.
3 is a flowchart of a method for processing game data in an embodiment of the present disclosure;
4 is a block diagram illustrating a module for processing game data according to an embodiment of the present disclosure.
5 is a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Various embodiments are now described with reference to the drawings. In this specification, various descriptions are presented to provide an understanding of the present disclosure. However, it is apparent that these embodiments may be practiced without these specific descriptions.

The terms “component,” “module,” “system,” and the like, as used herein, refer to a computer-related entity, hardware, firmware, software, a combination of software and hardware, or execution of software. For example, a component can be, but is not limited to being, a process running on a processor, a processor, an object, a thread of execution, a program, and/or a computer. For example, both an application running on a game server and a game server can be components. One or more components may reside within a processor and/or thread of execution. A component may be localized within one computer. A component may be distributed between two or more computers. In addition, these components can execute from various computer readable media having various data structures stored therein. Components may communicate via a network such as the Internet with another system, for example via a signal having one or more data packets (eg, data and/or signals from one component interacting with another component in a local system, distributed system, etc.) may communicate via local and/or remote processes depending on the data being transmitted).

In addition, the term “or” is intended to mean an inclusive “or” rather than an exclusive “or.” That is, unless otherwise specified or clear from context, "X employs A or B" is intended to mean one of the natural implicit substitutions. That is, X employs A; X employs B; or when X employs both A and B, "X employs A or B" may apply to either of these cases. It should also be understood that the term “and/or” as used herein refers to and includes all possible combinations of one or more of the listed related items.

Also, the terms "comprises" and/or "comprising" should be understood to mean that the feature and/or element in question is present. However, it should be understood that the terms "comprises" and/or "comprising" do not exclude the presence or addition of one or more other features, elements and/or groups thereof. Also, unless otherwise specified or unless the context is clear as to designating a singular form, the singular in the specification and claims should generally be construed to mean "one or more."

In addition, the term “at least one of A or B” should be interpreted as meaning “includes only A”, “includes only B”, and “in the case of a combination of A and B”.

Those skilled in the art will further appreciate that the various illustrative logical blocks, configurations, modules, circuits, means, logics, and algorithm steps described in connection with the embodiments disclosed herein may be implemented in electronic hardware, computer software, or combinations of both. It should be recognized that they can be implemented with To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, configurations, means, logics, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application. However, such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

Descriptions of the presented embodiments are provided to enable those skilled in the art to use or practice the present invention. Various modifications to these embodiments will be apparent to those skilled in the art of the present disclosure. The generic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present invention is not limited to the embodiments presented herein. This invention is to be construed in its widest scope consistent with the principles and novel features presented herein.

1 is a block diagram of a server for processing game data according to an embodiment of the present disclosure.

The configuration of the game server 100 shown in FIG. 1 is only a simplified example. In an embodiment of the present disclosure, the game server 100 may include other components for performing the computing environment of the game server 100 , and only some of the disclosed components may configure the game server 100 .

The game server 100 may include a processor 110 , a memory 130 , and a network unit 150 .

According to an embodiment of the present disclosure, the processor 110 may perform the following operations to process game data. The processor 110 may detect the workplace account by performing game data processing. The game data may include game log data. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the workplace may include players who use the game with a plurality of accounts or organizationally or collectively for commercial purposes. The workshop account may include a worker account that collects in-game goods into a bank account or a bank account that collects in-game goods. For example, a workshop may include a player who unfairly acquires, moves, or cashes items through "using illegal programs" or "speculative acts" with multiple accounts. Workshops may also contain players who, for example, acquire, move and cash items unfairly (repeatedly) with multiple accounts of similar and identical IPs. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 may generate a transaction network based on game log data. According to an embodiment of the present disclosure, the game log data may include all records performed by a character in the game in the game. The game log data may include action log data and history log data. The action log data may include an immediate record of the character's activity in the game. The history log data may include a record of a current state recorded at regular intervals in order to analyze game change details. Game log data includes, for example, character play time, account play time, frequency of use by item, personal store transaction items, item creation, item acquisition, item drop, item consumption, item creation, item destruction, item transaction, item enhancement, Character movement, character death, character level up, character creation, character attack, character defense, server start and end times, server response time, operator commands, monster creation, items currently worn by the character, etc. may include. The trading network may include a network representing interactions between users. According to an embodiment of the present disclosure, the transaction network may include a network indicating product transaction information between users. Specifically, the transaction network may include a network including information on transaction of goods or goods in the game between users in the game. A transaction network may be visualized based on a transaction adjacency matrix comprising user-to-user transaction information. The transaction adjacency matrix may include a matrix in which transaction information (eg, game goods, transaction presence, transaction price to average transaction price ratio) becomes an element of the matrix when there is a transaction, and 0 becomes an element of the matrix when there is no transaction. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 may generate a transaction network based on game log data. The processor 110 may calculate a transaction adjacency matrix based on the game log data. The transaction adjacency matrix may include a matrix including user-to-user transaction information. The processor 110 may generate a transaction network using the transaction adjacency matrix. Since the processor 110 creates a transaction network, it is possible to easily identify at a glance which node (eg, user account) collects the goods in the game. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the transaction network may include at least one of a node representing a user account and a trunk line including user-to-user transaction information. The edge may include a node and a line connecting the nodes. Edges can have directionality. An edge may include information on interactions between nodes and nodes. A node may contain user accounts. The user account may include a value capable of identifying a player playing the game. The user account may include, for example, a game ID, a character nickname, a user's resident registration number, a date of birth, an address, and the like. The transaction information may include quantitative information about a transaction that has occurred between users. The transaction information may include, for example, the amount of goods moved from user A to user B. The transaction information may include at least one of a movement direction of goods and a transaction weight. The goods movement direction may include the direction of goods moving from a specific user to another user. The goods movement direction may include a movement direction of the goods moving from the buyer to the seller. For example, when the goods move from the buyer to the seller, the processor 110 may determine the movement direction of the goods from the buyer to the seller as the movement direction of the goods. The transaction weight may include a measure indicating the degree of disadvantage in a transaction that the user has in a relationship with the counterparty. The transaction weight may be determined based on a difference in a transaction value between a transaction between the user and a counterparty and a transaction between another user. The transaction weight may be specifically determined based on a ratio of the average transaction price for the transaction target item and the transaction price between users. The transaction weight may include, for example, information that the user A purchases an item A having an average price of 100 won by paying 150 won, which is a higher price than that, and makes a transaction in which the user A loses 50 won. The foregoing is merely an example, and the present disclosure is not limited thereto.

로 표현될 수도 있다.

는 노드 i에서 노드 j로의 k번째 거래 가중치를 포함할 수 있다. 거래 가중치는 사용자간의 거래가를 거래 대상 물품에 대한 거래 평균가로 나눈 값을 포함할 수 있다. 본 개시의 일 실시예에 따라, 거래 가중치가 포함된 거래 인접 행렬이 개시된다. 거래 인접 행렬은 거래가 있는 경우 거래 정보(예를 들어, 게임 상의 재화, 거래 유무, 거래 평균가 대비 거래가 비율)이 행렬의 요소가 되고, 거래가 없으면 0이 행렬의 요소가 되는 행렬을 포함할 수 있다. 거래 인접 행렬은 복수개의 거래 인접 행렬 요소를 포함할 수 있다. 거래 인접 행렬의 요소는

로 표현될 수 있다. 프로세서(110)는 거래 인접 행렬 요소를 거래 가중치에 기초하여 결정할 수 있다. 거래 인접 행렬

는

(수학식 1)로 표현될 수도 있다. 거래 인접 행렬을 (수학식 1)로 정의함으로써 거래 인접 행렬 요소는

로 정규화 될 수 있다. 노드 i와 노드 j간의 상호 간 거래가 없는 경우, 프로세서(110)는 거래 인접 행렬 요소를

로 결정할 수 있다. 본 개시의 일 실시예에 따라, 거래 인접 행렬의 행 성분은 특정 노드의 다른 노드로부터의 구매 정보를 포함할 수 있다. 예를 들어, 행 성분은 특정 노드A로부터 다른 노드(B,C,D,E…)로 이동한 재화 정보를 포함할 수 있다. 거래 인접 행렬의 열 성분은 특정 노드로부터 다른 노드로의 판매 정보를 포함할 수 있다. 예를 들어, 열 성분은 다른 노드(B,C,D,E…)로부터 특정 노드A로 이동한 재화 정보를 포함할 수 있다. 프로세서(110)는 거래 인접 행렬을 이용하여 특정 노드로 이동하는 재화 정보를 산출할 수 있다. 거래 인접 행렬에 기초하여 거래 네트워크를 생성하므로, 프로세서(110)는 거래 네트워크를 기초로 특정 노드로 이동하는 재화 정보를 획득할 수 있다. 전술한 사항은 예시일 뿐 본 개시는 이에 제한되지 않는다.According to an embodiment of the present disclosure, the transaction weight may include a measure indicating the degree of disadvantage in a transaction that a user has in a relationship with a transaction counterparty. The transaction weight may be specifically determined based on a ratio of the average transaction price for the transaction target item and the transaction price between users. The trade weight is

may be expressed as

may include the k-th transaction weight from node i to node j. The transaction weight may include a value obtained by dividing a transaction price between users by an average transaction price for a transaction target item. According to an embodiment of the present disclosure, a transaction adjacency matrix including a transaction weight is disclosed. The transaction adjacency matrix may include a matrix in which transaction information (eg, goods in the game, transaction existence, transaction price to average transaction price) becomes an element of the matrix when there is a transaction, and 0 becomes an element of the matrix when there is no transaction. The transaction adjacency matrix may include a plurality of transaction adjacency matrix elements. The elements of the transaction adjacency matrix are

can be expressed as The processor 110 may determine the transaction adjacency matrix element based on the transaction weight. transaction adjacency matrix

is

It may be expressed as (Equation 1). By defining the transaction adjacency matrix as (Equation 1), the transaction adjacency matrix elements are

can be normalized to If there is no mutual transaction between node i and node j, the processor 110 calculates the transaction adjacency matrix element.

can be decided with According to an embodiment of the present disclosure, a row component of a transaction adjacency matrix may include purchase information from another node of a specific node. For example, the row element may include information on goods moved from a specific node A to another node (B, C, D, E...). The column component of the transaction adjacency matrix may contain sales information from one node to another. For example, the column component may include information on goods moved from other nodes (B, C, D, E...) to a specific node A. The processor 110 may calculate information on goods moving to a specific node by using the transaction adjacency matrix. Since the transaction network is generated based on the transaction adjacency matrix, the processor 110 may obtain information on goods moving to a specific node based on the transaction network. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 may divide the transaction network into a plurality of sub-transaction networks based on a predetermined criterion. The sub-transaction network may include a transaction network in which only at least some of the edges included in the transaction network are expressed. Alternatively, the sub-transaction network may include a transaction network in which only at least some nodes among nodes included in the transaction network are expressed. The processor 110 may perform workplace account detection using a plurality of sub-transaction networks divided based on a predetermined criterion. Workplace account detection is specifically described below. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 may divide the transaction network into a plurality of sub-transaction networks based on a predetermined criterion. The predetermined criterion may include a criterion for dividing the transaction network into a plurality of sub-transaction networks based on the transaction weight. The predetermined criterion may include at least one of a first threshold value or a second threshold value for the transaction weight. The processor 110 may determine the value of the first threshold value or the second threshold value in a direction to increase the accuracy of workplace detection through game log data analysis. The first threshold or the second threshold may also include a value arbitrarily determined by the game operator or user. The processor 110 may divide the transaction network into a plurality of sub-transaction networks using the first threshold or the second threshold. By dividing the transaction network into a plurality of sub-transaction networks, the processor 110 may acquire a sub-transaction network including information transacted at a high transaction weight and a sub-transaction network including information transacted at a low transaction weight. The processor 110 may know which node continuously performs a relatively unfavorable transaction (eg, continuously trades at a transaction price lower than the average price) through each sub-transaction network analysis. In addition, the processor 110 may know which node continuously performs a relatively advantageous transaction (eg, continuously trades at a transaction price higher than the average price) through the sub-transaction network analysis. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 may divide the transaction network into a plurality of sub-transaction networks based on a predetermined criterion. The processor 110 includes a first sub-transaction network in which the transaction weight is equal to or greater than the first threshold, a second sub-transaction network in which the transaction weight is less than the first threshold, and equal to or greater than the second threshold, and a third sub-transaction network in which the transaction weight is less than the second threshold. It can be divided into sub-transaction networks. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the first sub-transaction network may include transaction information in which a transaction is made at a price higher than the average transaction price. The first sub-transaction network may include a high-weight transaction network having an edge having a relatively high transaction weight. The processor 110 uses the high-weight transaction network 450 to generate a node in which sales prevail at a high price (eg, a node with more sales than purchase, a node with a high number of sales, a node with a large number of sales) and/or a node with a predominant purchase. can be detected. In a normal transaction, a transaction that is unilaterally favorable to the seller may not be concluded. A workplace account with an abnormal trading pattern can conduct a transaction even though it is a high-weight transaction that is unilaterally advantageous to the seller. Accordingly, the processor 110 may determine a node as a suspected workplace account node by detecting a node that transacts with a node with a dominant sale in the high-weight transaction network. That is, the processor 110 may determine a node that purchases a node at a high price from a node in which sales is dominant in the high-weight transaction network as a suspected workplace account node. More specifically, the worker node may purchase an item from the bank node at a transaction price higher than the average price in order to move the goods it owns to the bank node. Workplace accounts may include worker accounts or bank accounts. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the second sub-transaction network may include transaction information in which a transaction is made at an average transaction price. The second sub-trading network may include a medium-weighted trading network having an edge having a medium transaction weight. The processor 110 may analyze the normal transaction pattern by using the transaction network of the weighted value. The processor 110 may set a criterion for determining an abnormal transaction pattern by using a transaction network of a medium weight. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the third sub-transaction network may include transaction information in which a transaction is made at a price lower than the average transaction price. The third sub-trading network may include a low-weight trading network having an edge having a relatively low transaction weight. The processor 110 detects a node with a dominant sale at a low price (eg, a node with more sales than purchase, a node with a high number of sales, a node with a large number of sales) and/or a node with a predominant purchase using the low-value transaction network. can In a normal transaction, a transaction that is unilaterally favorable to the buyer may not be concluded. Workshop accounts with abnormal trading patterns can be sold even in low-weight transactions that are unilaterally advantageous to buyers. That is, the worker node can sell the item to the bank node at a price lower than the average price to move the item to the bank node. Accordingly, the processor 110 may determine a node in which sales prevails in the low-value transaction network as a suspected workplace account node. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to another embodiment of the present disclosure, the processor 110 may divide the transaction network into a plurality of sub-transaction networks based on a predetermined criterion. The processor 110 may be divided into a first sub-transaction network having a transaction weight equal to or greater than the first threshold value and a second sub-transaction network having a transaction weight less than the first threshold value. The processor 110 may divide the transaction network into a plurality (four or more) as needed in a direction to increase the workplace detection accuracy. The foregoing is merely an example, and the present disclosure is not limited thereto.

Hereinafter, a specific method for detecting a workplace account using a transaction network will be described in detail with reference to FIG. 2 .

According to an embodiment of the present disclosure, the processor 110 may determine a node included in at least one of the plurality of sub-transaction networks as the workplace account. The processor 110 may calculate a dominance score for each node included in the transaction network. The dominance score may include a score indicating the degree to which one node dominates another node in the network. For example, if one node is selling more than buying in relation to another node, the dominance score of the node that makes it dominant may be high. Also, as the amount of goods flowing out of a specific node increases and the amount of goods flowing into a node transacted with a specific node increases, the dominance score of a specific node may increase. The foregoing is merely an example, and the present disclosure is not limited thereto.

로 표현될 수 있다.

는 노드 i와 연결된 간선에 대응하는 거래 가중치들의 합을 의미할 수 있다.

는 노드i와 인접한 노드에 연결된 간선에 대응되는 거래 가중치들의 합을 의미할 수 있다. 이를 통해, 지배 점수는 특정 노드와 연결된 간선에 대한 정보만을 고려하는 것이 뿐만 아니라, 특정 노드와 인접한 노드와 연결된 간선에 대한 정보도 모두 포함할 수 있다. 따라서, 프로세서(110)는 노드별 지배 점수를 통하여 작업장 검출함에 있어 중요한 요소인 특정 노드의 거래 정보 뿐만 아니라 특정 노드와 거래한 노드의 거래 정보도 모두 고려할 수 있다. 이를 통해, 정확도 높은 작업장 검출을 수행할 수 있다. 본 개시의 일 실시예에 따라, 프로세서(110)가

를 통해 지배점수를 연산하는 경우 다음과 같은 상황이 발생할 수 있다. 낮은 랭크를 가진 노드(예를 들어, 판매에 비해 구매가 많은 노드)가 높은 랭크를 가진 노드에게 아이템을 판매할 경우, 프로세서(110)는 실제로 낮은 랭크를 가진 노드가 구매를 더 많이 했음에도 지배 점수가 높게 산출되어 판매를 많이 하는 노드로 인식할 수도 있다. 랭크는 노드로 유입되는 간선의 수를 포함할 수 있다. 또한 랭크는 노드로부터 유출되는 간선의 수를 포함할 수 있다. 본 개시에서, 랭크는 노드로 유입되는 적어도 하나의 간선에 대응되는 거래 가중치의 합 또는 노드로부터 유출되는 적어도 하나의 간선에 대응되는 거래 가중치의 합을 포함할 수 있다. 본 개시에서 사용되는 노드는 노드로 유입되는 (방향 그래프에서의) 간선의 수를 포함할 수 있다. 본 개시에서 사용되는 노드는 노드로 유입되는 적어도 하나의 간선에 대응되는 거래 가중치의 합일 수 있다. 전술한 사항은 예시일 뿐 본 개시는 이에 제한되지 않는다. According to an embodiment of the present disclosure, the processor 110 may calculate a dominance score for each node included in the transaction network. domination score

can be expressed as

may mean the sum of transaction weights corresponding to edges connected to node i.

may mean the sum of transaction weights corresponding to edges connected to node i and adjacent nodes. Through this, the dominance score may not only consider information on edges connected to a specific node, but also include all information on edges connected to a specific node and adjacent nodes. Accordingly, the processor 110 may consider not only transaction information of a specific node, which is an important factor in detecting a workplace, but also transaction information of a node transacted with a specific node, through the dominance score for each node. Through this, it is possible to perform high-accuracy workplace detection. According to an embodiment of the present disclosure, the processor 110

When calculating the dominance score through , the following situation may occur. When a node with a low rank (eg, a node with more purchases compared to sales) sells an item to a node with a high rank, the processor 110 actually generates a dominance score even though the node with a low rank makes more purchases. It can be recognized as a node that sells a lot because of the high value of . The rank may include the number of edges flowing into the node. The rank may also include the number of edges exiting the node. In the present disclosure, a rank may include a sum of transaction weights corresponding to at least one trunk flowing into a node or a sum of transaction weights corresponding to at least one trunk flowing out from a node. A node used in the present disclosure may include the number of edges (in a directed graph) entering a node. A node used in the present disclosure may be the sum of transaction weights corresponding to at least one trunk flowing into the node. The foregoing is merely an example, and the present disclosure is not limited thereto.

로 표현될 수 있다.

는 노드 i와 연결된 간선에 대응하는 제 1 거래 가중치들의 합을 의미할 수 있다.

는 노드i와 인접한 노드에 연결된 간선에 대응되는 제 2 거래 가중치들의 합을 의미할 수 있다. 선택 계수는 지배 점수에 반영되는 제 2 거래 가중치를 선택하기 위한 계수를 포함할 수 있다. 구체적으로 프로세서(110)는 선택 계수를 이용하여 지배 점수를 산출함에 있어 반영될 판매 관련 거래 가중치 또는 구매 관련 거래 가중치를 선택할 수 있다. 선택 계수는

(수학식 2) 및/또는

(수학식 3)에 기초하여 결정될 수 있다. 프로세서(110)는 클러스터링 알고리즘을 이용하여 "high" 또는 "low"를 구분할 수 있다. 클러스터링 알고리즘은 K=2인 K-means Clustering Algorithm을 포함할 수 있다. 프로세서(110)는 선택 계수를 이용함으로써 노드에 대한 잘못된 판단을 하는 상황을 방지할 수 있다. 예를 들어, 프로세서(110)는 선택 계수를 이용하여 지배 점수를 산출함으로써 실제로 낮은 랭크를 가진 노드가 구매를 더 많이 했음에도 지배 점수가 높게 산출됨으로써 판매를 많이 하는 노드로 인식하는 상황을 방지할 수 있다. 즉, 프로세서(110)가 선택 계수를 사용함으로써 지배 점수의 신뢰도를 높일 수 있다. 전술한 사항은 예시일 뿐 본 개시는 이에 제한되지 않는다.According to an embodiment of the present disclosure, the processor 110 may calculate a dominance score for each node included in the transaction network. The dominance score is based on at least one of a selection coefficient for selecting a first transaction weight corresponding to an edge connected to a node, a second transaction weight corresponding to an edge connected to the node and an adjacent node, or a second transaction weight reflected in the dominance score may include the determined score. domination score

can be expressed as

may mean the sum of the first transaction weights corresponding to the trunk line connected to the node i.

may mean the sum of second transaction weights corresponding to edges connected to node i and adjacent nodes. The selection coefficient may include a coefficient for selecting the second transaction weight reflected in the domination score. Specifically, the processor 110 may select a sales-related transaction weight or a purchase-related transaction weight to be reflected in calculating the dominance score by using the selection coefficient. The selection coefficient is

(Equation 2) and/or

It may be determined based on (Equation 3). The processor 110 may distinguish "high" or "low" using a clustering algorithm. The clustering algorithm may include a K-means Clustering Algorithm with K=2. The processor 110 may prevent a situation in which an erroneous determination of a node is made by using the selection coefficient. For example, the processor 110 calculates the dominance score using the selection coefficient to prevent a situation in which a node with a low rank is recognized as a node that makes a lot of sales by calculating a high dominance score even though a node with a lower rank actually makes more purchases. have. That is, the processor 110 may increase the reliability of the dominant score by using the selection coefficient. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 may determine a node included in at least one of the plurality of sub-transaction networks as the workplace account. The processor 110 may determine the first node 220 in which the rank of the node included in the first sub-transaction network 210 is equal to or greater than the predetermined first rank. The first node 220 may include a node having a high rank in the first sub-transaction network 210 . The first node 220 may include a node that sells to a number of nodes in the first sub-transaction network 210 . The rank may include the number of edges flowing into the node. According to an embodiment of the present disclosure, the rank of a node may include a sum of transaction weights corresponding to at least one trunk flowing into the node. According to an embodiment of the present disclosure, the predetermined first rank may include a criterion for detecting a node in which sales are dominant (eg, a node having a large number of sales, a node having a large sales volume). The processor 110 may determine a predetermined first rank for selecting a node having a high rank through game log data analysis. The predetermined first rank may be determined by a user or a game operator. For example, when the predetermined first rank is 1500 , the processor 110 may determine a node having a rank of 1500 or higher as the first node 220 . The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 reselects at least some of the first nodes based on the number of nodes that have transacted with the determined node (first node) to determine the second node by reselecting the first filtering (230) can be performed. The first filtering 230 may include filtering in order to select only nodes whose number of nodes transacted with the first node 220 is greater than or equal to a certain number (eg, more than 7 nodes) among the first nodes 220 . have. The first filtering 230 may include filtering for calculating the seller accounts in which the number of nodes directly transacted with the seller is greater than or equal to a certain number. The first filtering 230 may be performed based on density-based clustering. Accordingly, the processor 110 may remove an outlier node that is not a necessary node in the process of detecting the workplace account through the first filtering. Through this, it is possible to find workplaces with high accuracy by filtering workplace suspicious accounts by reflecting workplace transaction patterns (for example, one black consumer suspicious account taking unfair advantage is seller and multiple workplace suspicious accounts are buyers). can The processor 110 may or may not perform the first filtering 230 to detect the workplace. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 may determine a node included in at least one of the plurality of sub-transaction networks as the workplace account. When the rank of a node included in the third sub-transaction network is higher than or equal to a predetermined second rank, the processor 110 may determine the third node as the third node. The third node 260 may include a node having a high rank in the third sub-transaction network 250 . The third node 260 may include a node that sells to a number of nodes in the third sub-transaction network 250 . According to an embodiment of the present disclosure, the predetermined second rank may include a criterion for detecting a node (eg, a node with a large number of sales, a node with a large sales volume) in the third sub-transaction network in which sales are dominant. . The second predetermined rank may have a value equal to or different from the predetermined first rank. The processor 110 may determine a predetermined second rank for selecting a node having a high rank through game log data analysis. The predetermined second rank may be determined by a user or a game operator. For example, when the predetermined second rank is 1000, the processor 110 may determine a node having a rank of 1000 or more as the third node 260 . The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 re-selects at least a portion of the third nodes 260 based on the number of nodes transacted with the determined node (third node) to determine the fourth node. 2 filtering 270 may be performed. The second filtering 270 may include filtering only the nodes in which the number of nodes transacted with the third node among the third nodes is less than or equal to a certain number (eg, the number of nodes is less than or equal to 16). The second filtering 270 may include filtering to calculate a purchaser account in which the number of purchasers who directly transacted with a specific node is less than or equal to a certain number. The second filtering 270 may be performed based on density-based clustering. Accordingly, the processor 110 may remove outlier nodes that are not workplace accounts through the second filtering. Through this, by reflecting workplace transaction patterns (for example, one suspicious workplace account is a seller and a large number of black consumer suspicious accounts taking unfair advantage are buyers), it is possible to find workplaces with high accuracy by filtering workplace suspicious accounts. can pay The processor 110 may or may not perform the second filtering 270 to detect the workplace. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 may determine a node included in at least one of the plurality of sub-transaction networks as the workplace account. When the node transacted with the first node and the third node are the same node, the processor 110 may determine the node as the workshop account. The processor 110 may calculate the intersection of the node transacted with the first node and the third node. In addition, the processor 110 may determine the intersection of the node transacted with the first node and the third node as the workplace account. In addition, according to an embodiment of the present disclosure, the processor 110 may determine the node as the workplace account when the filtered fourth node and the filtered second node 240 are the same node. The processor 110 may calculate the intersection 280 of the node transacted with the second node and the fourth node. In addition, the processor 110 may determine the intersection 280 of the node transacted with the second node and the fourth node as the workplace account. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the processor 110 may extract an existing workplace transaction pattern based on game log data analysis. The workplace transaction pattern may include a transaction pattern in which a transaction is repeatedly performed between two nodes for the same item. A recurring transaction may include a transaction in which one node (e.g., a worker node) sells the same item at a lower price and the other node (e.g., a bank node) sells the same item back to the worker node at a higher price. have. Accordingly, it may result in goods being biased toward one node (eg, a bank node). A node on which goods are concentrated may mean a bank node. In addition, an account that continuously supplies goods and/or items to a bank account may refer to a worker node. The workplace trading pattern may include a trading pattern of nodes that continuously sell at low prices (ie, nodes that continuously sell at low prices). A node that continuously sells at a low weight may include a worker node. Workplace trading patterns may also include trading patterns of nodes that consistently sell at high weights (ie, nodes that consistently sell at high prices). Nodes selling at high weight may include bank nodes. Accordingly, when a node having a high rank in the high-weight transaction network and a node having a transaction with a node having a high rank in the low-weight transaction network coincide with a node having a high rank in the low-weight transaction network, the processor 110 may determine the corresponding node as the workshop account. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to the present disclosure, game companies impose certain sanctions on accounts determined as workplace accounts. Certain sanctions may include, for example, permanent game suspension, game temporary suspension, transaction ban, and the like. If sanctions are applied, the user may be unable to perform normal game play and may leave the game. If a normal user is judged to be a work place account and sanctioned, the normal user may become dissatisfied with the game and leave the game. Therefore, detecting and sanctioning the workplace with high accuracy can be a problem directly related to the rise and fall of the game. Therefore, according to the present disclosure, by using the transaction network divided based on the transaction weight and the dominance score for each node to which the selection coefficient is applied, the processor 110 can detect the workplace account with high accuracy. Through this, it is possible to prevent the normal users from being damaged and to prevent the departure of game users.

According to an embodiment of the present disclosure, since high-accuracy workplace detection is important, the processor 110 determines the intersection of the adjacent node of the second node and the fourth node as the workplace in order to increase the workplace detection accuracy, and then performs a verification operation. can A specific method for the verification operation is described below.

According to an embodiment of the present disclosure, the workshop pattern may include a pattern related to level distribution for each node. The workshop pattern may include pattern information in which the game level of the first node included in the first sub-trading network is high and the game level of the third node included in the third sub-transaction network is low. Accordingly, the processor 110 determines whether the game level of the first node included in the first sub-trading network is high and/or based on whether the game level of the third node included in the third sub-trading network is low. It can be verified by finally evaluating whether the node determined by The above-described verification operation is merely an example and the present disclosure is not limited thereto.

According to another embodiment of the present disclosure, the workshop pattern may include a pattern related to the payment level of the node. The workshop pattern may include pattern information whether the first node included in the first sub-transaction network has a large amount of payment or whether the third node included in the third sub-transaction network has a small amount of payment. Accordingly, the processor 110 determines whether the first node included in the first sub-transaction network has a large amount of payment and/or whether the third node included in the third sub-transaction network has a small amount of payment. It can be verified by finally evaluating whether it is a workplace. The above-described verification operation is merely an example, and the present disclosure is not limited thereto.

According to another embodiment of the present disclosure, the workplace transaction pattern may include a pattern related to an IP type. The workplace pattern may include a pattern in which nodes of a similar level are distributed in the same IP type. The processor 110 may finally evaluate and verify whether the node determined as the work site is the work site based on whether nodes of a similar level to the same IP type are distributed. The above-described verification operation is merely an example, and the present disclosure is not limited thereto.

2 is an exemplary diagram for explaining a method of detecting a workplace account according to an embodiment of the present disclosure.

In FIG. 2 , the first sub-transaction network 210, the first node 220 extracted from the first sub-transaction network 210, the first filtering 230, and the second obtained as a result of the first filtering 230 Neighbor node 240 extraction of node, third sub-transaction network 250, third node 260 extracted from third sub-transaction network 250, second filtering 270, and neighbor node of the second node and the intersection 280 of the fourth node is shown. The game server 100 may determine a node included in the intersection 280 of the adjacent node of the second node and the fourth node as the workplace account.

According to an embodiment of the present disclosure, the game server 100 may divide the transaction network into a plurality of sub-transaction networks based on a predetermined criterion. The predetermined criterion may include a criterion for dividing the transaction network into a plurality of sub-transaction networks based on the transaction weight. The predetermined criterion may include at least one of a first threshold value or a second threshold value for the transaction weight. The game server 100 may determine the value of the first threshold value or the second threshold value in a direction to increase the accuracy of workplace detection through game log data analysis. The first threshold or the second threshold may also include a value arbitrarily determined by the game operator or user. The game server 100 may divide the transaction network into a plurality of sub-transaction networks by using the first threshold or the second threshold. By dividing the transaction network into a plurality of sub-trading networks, the game server 100 may acquire a sub-transaction network including information transacted at a high transaction weight and a sub-transaction network including information transacted at a low transaction weight. The game server 100 may know which node continuously performs a relatively unfavorable transaction (eg, continuously trades at a transaction price lower than the average price) through each sub-transaction network analysis. In addition, the game server 100 may know which node continuously performs a relatively advantageous transaction (eg, continuously trades at a transaction price higher than the average price) through the sub-transaction network analysis. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the game server 100 may divide the transaction network into a plurality of sub-transaction networks based on a predetermined criterion. The game server 100 includes a first sub-transaction network 210 having a transaction weight equal to or greater than a first threshold, a second sub-trading network having a transaction weight less than the first threshold and greater than or equal to the second threshold, and a second sub-trading network having a transaction weight equal to or greater than the second threshold. It can be divided into a third sub-transaction network 250 that is less than the value. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the game server 100 may determine a node included in at least one of the plurality of sub-transaction networks as the workplace account. The game server 100 may calculate a domination score for each node included in the transaction network. The dominance score may include a score indicating the degree to which one node dominates another node in the network. For example, as the amount of goods flowing out of a specific node increases and the amount of goods flowing into a node transacted with a specific node increases, the dominance score of a specific node may increase. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the game server 100 may determine a node included in at least one of the plurality of sub-transaction networks as the workplace account. The game server 100 may determine the first node 220 in which the rank of the node included in the first sub-transaction network 210 is equal to or greater than the predetermined first rank. The first node 220 may include a node having a high rank in the first sub-transaction network 210 . The first node 220 may include a node that sells to a number of nodes in the first sub-transaction network 210 . The rank may include the number of edges flowing into the node. According to an embodiment of the present disclosure, the rank of a node may include a sum of transaction weights corresponding to at least one trunk flowing into the node. According to an embodiment of the present disclosure, the predetermined first rank may include a criterion for detecting a node in which sales are dominant (eg, a node having a large number of sales, a node having a large sales volume). The game server 100 may determine a predetermined first rank for selecting a node having a high rank through game log data analysis. The predetermined first rank may be determined by a user or a game operator. For example, when the predetermined first rank is 1500 , the game server 100 may determine a node having a rank of 1500 or higher as the first node 220 . The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the game server 100 reselects at least some of the first nodes based on the determined number of nodes (first nodes) and the number of nodes transacted with the first node to determine the second node. Filtering 230 may be performed. The first filtering 230 may include filtering in order to select only nodes whose number of nodes transacted with the first node 220 is greater than or equal to a certain number (eg, more than 7 nodes) among the first nodes 220 . have. The first filtering 230 may include filtering for calculating the seller accounts in which the number of nodes directly transacted with the seller is greater than or equal to a certain number. The first filtering 230 may be performed based on density-based clustering. Accordingly, the game server 100 may remove an outlier node, which is not a necessary node, through the first filtering in the process of detecting the workplace account. Through this, it is possible to find workplaces with high accuracy by filtering workplace suspicious accounts by reflecting workplace transaction patterns (for example, one black consumer suspicious account taking unfair advantage is seller and multiple workplace suspicious accounts are buyers). can The game server 100 may or may not perform the first filtering 230 to detect the workplace. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the game server 100 may determine a node included in at least one of the plurality of sub-transaction networks as the workplace account. When the rank of the node included in the third sub-transaction network 250 is higher than or equal to the second rank, the game server 100 may determine it as the third node. The third node 260 may include a node having a high rank in the third sub-transaction network 250 . The third node 260 may include a node that sells to a number of nodes in the third sub-transaction network 250 . According to an embodiment of the present disclosure, the predetermined second rank includes a criterion for detecting a node (eg, a node with a large number of sales, a node with a large sales volume) in the third sub-transaction network 250 . can do. The second predetermined rank may have a value equal to or different from the predetermined first rank. The game server 100 may determine a predetermined second rank for selecting a node having a high rank through game log data analysis. The predetermined second rank may be determined by a user or a game operator. For example, when the predetermined second rank is 1000 , the game server 100 may determine a node having a rank of 1000 or more as the third node 260 . The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the game server 100 determines the fourth node by reselecting at least a portion of the third nodes 260 based on the number of nodes transacted with the determined node (third node). A second filtering 270 may be performed. The second filtering 270 may include filtering only the nodes in which the number of nodes transacted with the third node among the third nodes is less than or equal to a certain number (eg, the number of nodes is less than or equal to 16). The second filtering 270 may include filtering to calculate a purchaser account in which the number of purchasers who directly transacted with a specific node is less than or equal to a certain number. The second filtering 270 may be performed based on density-based clustering. Accordingly, the game server 100 may remove outlier nodes that are not workplace accounts through the second filtering. Through this, by reflecting workplace transaction patterns (for example, one suspicious workplace account is a seller and a large number of black consumer suspicious accounts taking unfair advantage are buyers), it is possible to find workplaces with high accuracy by filtering workplace suspicious accounts. can pay The game server 100 may or may not perform the second filtering 270 to detect the workplace. The foregoing is merely an example, and the present disclosure is not limited thereto.

According to an embodiment of the present disclosure, the game server 100 may determine a node included in at least one of the plurality of sub-transaction networks as the workplace account. The game server 100 may determine the node as the workshop account when the node transacted with the first node and the third node are the same node. The game server 100 may calculate the intersection of the node transacted with the first node and the third node. In addition, the game server 100 may determine the intersection of the node transacted with the first node and the third node as the workplace account. In addition, according to an embodiment of the present disclosure, the game server 100 may determine the node as the workplace account when the filtered second node and the transaction node 240 and the filtered fourth node are the same node. The game server 100 may calculate the intersection 280 of the node transacted with the second node and the fourth node. In addition, the game server 100 may determine the intersection 280 of the node transacted with the second node and the fourth node as the workplace account. The foregoing is merely an example, and the present disclosure is not limited thereto.

3 is a flowchart of a method for processing game data in an embodiment of the present disclosure;

According to an embodiment of the present disclosure, a transaction network may be generated 510 based on game log data.

According to an embodiment of the present disclosure, the transaction network may include at least one of a node representing a user account and a trunk line including user-to-user transaction information.

According to an embodiment of the present disclosure, the transaction information is quantitative information about a transaction generated between users, and may include at least one of a movement direction of goods or a transaction weight.

According to an embodiment of the present disclosure, the transaction weight is a measure indicating the degree of disadvantage in a transaction that the user has in a relationship with the counterparty, and is determined based on the difference in the transaction value between the transaction between the user and the counterparty and the transaction of another user. can

According to an embodiment of the present disclosure, the predetermined criterion is a criterion for dividing the transaction network into a plurality of sub-transaction networks based on the transaction weight, and includes at least one of a first threshold value or a second threshold value for the transaction weight can do.

According to an embodiment of the present disclosure, the transaction network may be divided ( 520 ) into a plurality of sub-transaction networks based on a predetermined criterion.

According to an embodiment of the present disclosure, the domination score may include a score determined based on a quantitative amount of game goods flowing out from the node.

According to an embodiment of the present disclosure, the dominance score includes a first transaction weight corresponding to an edge connected to a node, a second transaction weight corresponding to an edge connected to a node adjacent to the node, or a second transaction weight reflected in the domination score. and a score determined based on at least one of selection coefficients for selection.

According to an embodiment of the present disclosure, a node included in at least one of the plurality of sub-transaction networks may be determined 530 as the workplace account.

4 is a block diagram illustrating a module for processing game data according to an embodiment of the present disclosure.

According to an embodiment of the present disclosure, game data processing may be implemented by the following module.

According to an embodiment of the present disclosure, a module 610 for generating a transaction network based on game log data; a module 620 for dividing the trading network into a plurality of sub-transaction networks based on a predetermined criterion; and a module 630 for determining a node included in at least one of the plurality of sub-transaction networks as the workplace account.

In an alternative embodiment for processing game data, the module 620 for dividing the trading network into a plurality of sub-transaction networks based on a predetermined criterion comprises a first sub-transaction network, a transaction in which a transaction weight is equal to or greater than a first threshold, and a module for dividing into a second sub-transaction network whose weight is less than the first threshold value and greater than or equal to the second threshold value, and a third sub-transaction network whose transaction weight is less than the second threshold value.

In an alternative embodiment for processing game data, the module for determining a node included in at least one of the plurality of sub-transaction networks as a workplace account may include a module for calculating a dominance score for each node included in the trading network. can

In an alternative embodiment for processing game data, the module 630 for determining a node included in at least one of a plurality of sub-transaction networks as a workplace account is configured such that the rank of the node included in the first sub-transaction network is determined in advance. and a module for determining a first node of a first rank or higher. The rank of the node may include at least one of a sum of transaction weights corresponding to at least one trunk flowing into the node or a sum of transaction weights corresponding to at least one trunk flowing out from the node.

In an alternative embodiment for processing game data, further comprising a module for performing a first filtering to determine a second node by re-selecting at least some of the first nodes based on the number of nodes that have transacted with the determined nodes can do.

In an alternative embodiment for processing game data, the module 630 for determining a node included in at least one of a plurality of sub-transaction networks as a workplace account is configured such that the rank of the node included in the third sub-transaction network is predetermined. and a module for determining a third node having a second rank or higher.

In an alternative embodiment for processing game data, a module for performing a second filtering to determine a fourth node by re-selecting at least some of the third nodes based on the number of nodes that have transacted with the determined nodes. can

In an alternative embodiment for processing game data, the module 630 for determining a node included in at least one of a plurality of sub-transaction networks as a workplace account is a node in which the node transacted with the first node and the third node are the same. a module for determining a node as a workshop account; Alternatively, when a node transacted with the filtered second node and the filtered fourth node are the same node, at least one of a module for determining a node as a workplace account may be included.

5 is a simplified, general schematic diagram of an exemplary computing environment in which embodiments of the present disclosure may be implemented.

Although the present disclosure has been described above generally as being capable of being implemented by a computing device, those skilled in the art will appreciate that the present disclosure is a combination of hardware and software and/or in combination with computer-executable instructions and/or other program modules that may be executed on one or more computers It will be appreciated that it can be implemented as a combination.

Generally, program modules include routines, programs, components, data structures, etc. that perform particular tasks or implement particular abstract data types. In addition, those skilled in the art will appreciate that the methods of the present disclosure are suitable for single-processor or multiprocessor computer systems, minicomputers, mainframe computers as well as personal computers, handheld computing devices, microprocessor-based or programmable consumer electronics, etc. (each of which It will be appreciated that other computer system configurations may be implemented, including those that may operate in connection with one or more associated devices.

The described embodiments of the present disclosure may also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Computers typically include a variety of computer-readable media. Any medium accessible by a computer can be a computer readable medium, and such computer readable media includes volatile and nonvolatile media, transitory and non-transitory media, removable and non-transitory media. including removable media. By way of example, and not limitation, computer-readable media may include computer-readable storage media and computer-readable transmission media. Computer-readable storage media includes volatile and non-volatile media, transitory and non-transitory media, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. includes media. A computer-readable storage medium may be RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage device, magnetic cassette, magnetic tape, magnetic disk storage device, or other magnetic storage device. device, or any other medium that can be accessed by a computer and used to store the desired information.

Computer readable transmission media typically embodies computer readable instructions, data structures, program modules or other data, etc. in a modulated data signal such as a carrier wave or other transport mechanism, and Includes any information delivery medium. The term modulated data signal means a signal in which one or more of the characteristics of the signal is set or changed so as to encode information in the signal. By way of example, and not limitation, computer-readable transmission media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared, and other wireless media. Combinations of any of the above are also intended to be included within the scope of computer-readable transmission media.

An example environment 1100 implementing various aspects of the disclosure is shown including a computer 1102 , the computer 1102 including a processing unit 1104 , a system memory 1106 , and a system bus 1108 . do. The system bus 1108 couples system components, including but not limited to system memory 1106 , to the processing device 1104 . The processing device 1104 may be any of a variety of commercially available processors. Dual processor and other multiprocessor architectures may also be used as processing unit 1104 .

The system bus 1108 may be any of several types of bus structures that may further be interconnected to a memory bus, a peripheral bus, and a local bus using any of a variety of commercial bus architectures. System memory 1106 includes read only memory (ROM) 1110 and random access memory (RAM) 1112 . A basic input/output system (BIOS) is stored in non-volatile memory 1110, such as ROM, EPROM, EEPROM, etc., which is the basic input/output system (BIOS) that helps transfer information between components within computer 1102, such as during startup contains routines. RAM 1112 may also include high-speed RAM, such as static RAM, for caching data.

The computer 1102 may also be configured for external use within an internal hard disk drive (HDD) 1114 (eg, EIDE, SATA) - this internal hard disk drive 1114 may also be configured for external use within a suitable chassis (not shown). Yes-, magnetic floppy disk drive (FDD) 1116 (eg, for reading from or writing to removable diskette 1118), and optical disk drive 1120 (eg, CD-ROM) for reading from, or writing to, disk 1122, or other high capacity optical media such as DVD. The hard disk drive 1114 , the magnetic disk drive 1116 , and the optical disk drive 1120 are connected to the system bus 1108 by the hard disk drive interface 1124 , the magnetic disk drive interface 1126 , and the optical drive interface 1128 , respectively. ) can be connected to The interface 1124 for implementing an external drive includes at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies.

These drives and their associated computer-readable media provide non-volatile storage of data, data structures, computer-executable instructions, and the like. In the case of computer 1102, drives and media correspond to storing any data in a suitable digital format. Although the description of computer readable media above refers to HDDs, removable magnetic disks, and removable optical media such as CDs or DVDs, those skilled in the art will use zip drives, magnetic cassettes, flash memory cards, cartridges, etc. It will be appreciated that other tangible computer-readable media such as etc. may also be used in the exemplary operating environment and any such media may include computer-executable instructions for performing the methods of the present disclosure.

A number of program modules may be stored in the drive and RAM 1112 , including an operating system 1130 , one or more application programs 1132 , other program modules 1134 , and program data 1136 . All or portions of the operating system, applications, modules, and/or data may also be cached in RAM 1112 . It will be appreciated that the present disclosure may be implemented in various commercially available operating systems or combinations of operating systems.

A user may enter commands and information into the computer 1102 via one or more wired/wireless input devices, for example, a pointing device such as a keyboard 1138 and a mouse 1140 . Other input devices (not shown) may include a microphone, IR remote control, joystick, game pad, stylus pen, touch screen, and the like. Although these and other input devices are often connected to the processing unit 1104 through an input device interface 1142 that is connected to the system bus 1108, parallel ports, IEEE 1394 serial ports, game ports, USB ports, IR interfaces, It may be connected by other interfaces, etc.

A monitor 1144 or other type of display device is also coupled to the system bus 1108 via an interface, such as a video adapter 1146 . In addition to the monitor 1144, the computer typically includes other peripheral output devices (not shown), such as speakers, printers, and the like.

Computer 1102 may operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1148 via wired and/or wireless communications. Remote computer(s) 1148 may be workstations, computing device computers, routers, personal computers, portable computers, microprocessor-based entertainment devices, peer devices, or other common network nodes, and are typically connected to computer 1102 . Although it includes many or all of the components described for it, only memory storage device 1150 is shown for simplicity. The logical connections shown include wired/wireless connections to a local area network (LAN) 1152 and/or a larger network, eg, a wide area network (WAN) 1154 . Such LAN and WAN networking environments are common in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which can be connected to a worldwide computer network, for example, the Internet.

When used in a LAN networking environment, the computer 1102 is connected to the local network 1152 through a wired and/or wireless communication network interface or adapter 1156 . Adapter 1156 may facilitate wired or wireless communication to LAN 1152 , which also includes a wireless access point installed therein for communicating with wireless adapter 1156 . When used in a WAN networking environment, the computer 1102 may include a modem 1158, be connected to a communication computing device on the WAN 1154, or establish communications over the WAN 1154, such as over the Internet. have other means. A modem 1158 , which may be internal or external and a wired or wireless device, is coupled to the system bus 1108 via a serial port interface 1142 . In a networked environment, program modules described for computer 1102 , or portions thereof, may be stored in remote memory/storage device 1150 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communication link between the computers may be used.

Computer 1102 may be associated with any wireless device or object that is deployed and operates in wireless communication, for example, printers, scanners, desktop and/or portable computers, portable data assistants (PDAs), communications satellites, wireless detectable tags. It operates to communicate with any device or place, and phone. This includes at least Wi-Fi and Bluetooth wireless technologies. Accordingly, the communication may be a predefined structure as in a conventional network or may simply be an ad hoc communication between at least two devices.

Wi-Fi (Wireless Fidelity) makes it possible to connect to the Internet, etc. without a wire. Wi-Fi is a wireless technology such as cell phones that allows these devices, eg, computers, to transmit and receive data indoors and outdoors, ie anywhere within range of a base station. Wi-Fi networks use a radio technology called IEEE 802.11 (a, b, g, etc.) to provide secure, reliable, and high-speed wireless connections. Wi-Fi can be used to connect computers to each other, to the Internet, and to wired networks (using IEEE 802.3 or Ethernet). Wi-Fi networks may operate in unlicensed 2.4 and 5 GHz radio bands, for example at 11 Mbps (802.11a) or 54 Mbps (802.11b) data rates, or in products that include both bands (dual band). .

One of ordinary skill in the art of this disclosure will understand that information and signals may be represented using any of a variety of different technologies and techniques. For example, data, instructions, instructions, information, signals, bits, symbols and chips that may be referenced in the above description are voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields particles or particles, or any combination thereof.

Those of ordinary skill in the art of the present disclosure will recognize that the various illustrative logical blocks, modules, processors, means, circuits, and algorithm steps described in connection with the embodiments disclosed herein include electronic hardware, (convenience For this purpose, it will be understood that it may be implemented by various forms of program or design code (referred to herein as software) or a combination of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. A person skilled in the art of the present disclosure may implement the described functionality in various ways for each specific application, but such implementation decisions should not be interpreted as a departure from the scope of the present disclosure.

The various embodiments presented herein may be implemented as methods, apparatus, or articles of manufacture using standard programming and/or engineering techniques. The term article of manufacture includes a computer program, carrier, or media accessible from any computer-readable storage device. For example, computer-readable storage media include magnetic storage devices (eg, hard disks, floppy disks, magnetic strips, etc.), optical disks (eg, CDs, DVDs, etc.), smart cards, and flash drives. memory devices (eg, EEPROMs, cards, sticks, key drives, etc.). Also, various storage media presented herein include one or more devices and/or other machine-readable media for storing information.

It is understood that the specific order or hierarchy of steps in the presented processes is an example of exemplary approaches. Based on design priorities, it is understood that the specific order or hierarchy of steps in the processes may be rearranged within the scope of the present disclosure. The appended method claims present elements of the various steps in a sample order, but are not meant to be limited to the specific order or hierarchy presented.

The description of the presented embodiments is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the scope of the present disclosure. Thus, the present disclosure is not intended to be limited to the embodiments presented herein, but is to be construed in the widest scope consistent with the principles and novel features presented herein.

Claims (16)

A computer program stored on a computer-readable storage medium, wherein the computer program, when executed on one or more processors, causes the following operations to perform game data processing, the operations comprising:
generating a trading network based on the game log data;
dividing the trading network into a plurality of sub-transaction networks based on a predetermined criterion; and
determining a node included in at least one of the plurality of sub-transaction networks as a workplace account;
comprising,
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The trading network is
At least one of a node meaning a user account or a trunk line including user-to-user transaction information,
A computer program stored on a computer-readable storage medium.
3. The method of claim 2,
The transaction information is
Quantitative information about transactions that have occurred between users,
At least one of the movement direction of the goods or the transaction weight,
A computer program stored on a computer-readable storage medium.
4. The method of claim 3,
The transaction weight is
As a measure of the degree of disadvantage in a transaction that a user has in a relationship with the counterparty, it is determined based on the difference in the transaction value between the transaction between the user and the counterparty and that of another user;
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The predetermined criterion is
As a criterion for dividing the trading network into a plurality of sub-transaction networks based on the transaction weight,
at least one of a first threshold value or a second threshold value for the transaction weight;
A computer program stored on a computer-readable storage medium.
6. The method of claim 5,
The operation of dividing the trading network into a plurality of sub-transaction networks based on the predetermined criterion comprises:
a first sub-transaction network in which the transaction weight is equal to or greater than the first threshold;
a second sub-trading network in which the transaction weight is less than the first threshold and greater than or equal to the second threshold, and
and dividing the transaction weight into a third sub-transaction network whose transaction weight is less than the second threshold.
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The operation of determining a node included in at least one of the plurality of sub-transaction networks as a workplace account includes:
calculating a dominance score for each node included in the transaction network;
comprising,
A computer program stored on a computer-readable storage medium.
8. The method of claim 7,
The dominance score is
A score determined based on the quantitative amount of game goods flowing out of the node,
A computer program stored on a computer-readable storage medium.
8. The method of claim 7,
The dominance score is
a first transaction weight corresponding to an edge connected to the node;
a second transaction weight corresponding to an edge connected to a node adjacent to the node; or
a score determined based on at least one of a selection coefficient for selecting a second transaction weight reflected in the domination score;
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The operation of determining a node included in at least one of the plurality of sub-transaction networks as a workplace account includes:
determining a first node in which a rank of a node included in the first sub-transaction network is higher than or equal to a predetermined first rank;
including,
The rank of the node is
The sum of the transaction weights corresponding to at least one edge flowing into the node, or
Sum of transaction weights corresponding to at least one edge flowing out of the node
at least one of
A computer program stored on a computer-readable storage medium.
11. The method of claim 10,
performing first filtering to determine a second node by reselecting at least some of the first nodes based on the determined number of nodes transacted with the node;
further comprising,
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The operation of determining a node included in at least one of the plurality of sub-transaction networks as a workplace account includes:
determining a third node in which the rank of the node included in the third sub-transaction network is higher than or equal to a predetermined second rank;
comprising,
A computer program stored on a computer-readable storage medium.
13. The method of claim 12,
performing second filtering to determine a fourth node by reselecting at least some of the third nodes based on the determined number of nodes transacted with the node;
further comprising,
A computer program stored on a computer-readable storage medium.
The method of claim 1,
The operation of determining a node included in at least one of the plurality of sub-transaction networks as a workplace account includes:
determining the node as a workplace account when the node transacted with the first node and the third node are the same node; or
determining the node as a workplace account when the node that has transacted with the filtered second node and the filtered fourth node are the same node;
comprising at least one of
A computer program stored on a computer-readable storage medium.
A method for processing game data, comprising:
generating a trading network based on the game log data;
dividing the trading network into a plurality of sub-transaction networks based on a predetermined criterion; and
determining a node included in at least one of the plurality of sub-transaction networks as a workplace account;
containing,
Methods for processing game data
A game server for processing game data, comprising:
one or more processors; and
a memory storing instructions executable by the one or more processors;
including,
The one or more processors,
create a trading network based on game log data,
dividing the trading network into a plurality of sub-transaction networks based on a predetermined criterion, and
Determining a node included in at least one of the plurality of sub-transaction networks as a workplace account,
A game server for processing game data.

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