CN107909496A - User influence in social network analysis method, device and electronic equipment - Google Patents
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Abstract
This application discloses a kind of user influence in social network analysis method, device and electronic equipment, this method includes：According to the angle value of each node corresponding with each user in the social networks of user, the jumping degree of each node is determined；The angle value is determined by communication behavior data between each user；When the angle value of the node in each node with maximum jumping degree meets preset condition, it is influential target person to determine the corresponding user of the node；The preset condition is that one or more parameters exceed threshold value, realizes and user and its social networks are effectively analyzed, and accurately analyze target person influential in the social networks.
Description
Technical Field
The application relates to the technical field of internet, in particular to a method and a device for analyzing user influence in a social network and electronic equipment.
Background
With the rapid development of the internet technology, social networks have come to the fore, and the social networks have the characteristics of large user scale, rapid topic information transmission, wide influence range and the like. The rapid development of the social network has become a new type of relay medium for new information carriers, and the influence on the daily work and life of people is increasing. Social networks, in which the influence of each person is different, connect people together. Therefore, there is a need to analyze a target person, e.g., a leader, in the social network.
The current methods of analyzing influence of users in social networks focus on content analysis of user behavior, such as by what the user said? What was evaluated? What was forwarded? The user is then tagged with labels such as gender, age, etc. And then, performing group portrayal on the user by using the label so as to perform modeling analysis. However, the method only realizes the analysis of the individual behaviors of the user in the whole social network, and cannot realize the effective analysis of the user and the social network thereof and accurately analyze the target person in the social network.
Content of application
The embodiment of the application provides a method for analyzing influence of a user in a social network, which is used for solving the problems that effective analysis on the user and the social network cannot be realized and a target person in the social network cannot be accurately analyzed in the prior art.
The embodiment of the application further provides a user influence analysis device in the social network, which is used for solving the problems that effective analysis cannot be performed on the user and the social network thereof and target characters in the social network can not be accurately analyzed in the prior art.
The embodiment of the application further provides electronic equipment for solving the problems that effective analysis cannot be performed on the user and the social network of the user in the prior art, and a target person in the social network cannot be accurately analyzed.
The embodiment of the application adopts the following technical scheme:
in a first aspect, a method for analyzing influence of a user in a social network is provided, where the method includes:
determining the jumping degree of each node according to the value of each node corresponding to each user in the social network of the user; the value is determined by communication behavior data among all users;
when the value of the node with the maximum jumping degree in each node meets a preset condition, determining that a user corresponding to the node is an influential target person; the preset condition is that one or more parameters exceed a threshold value.
In a second aspect, an apparatus for analyzing influence of users in a social network is provided, the apparatus comprising:
the jumping degree determining module is used for determining the jumping degree of each node according to the value of each node corresponding to each user in the social network of the user; the value is determined by communication behavior data among all users;
the target person determining module is used for determining that the user corresponding to the node is the target person with influence when the value of the node with the maximum jumping degree in each node meets a preset condition; the preset condition is that one or more parameters exceed a threshold value.
In a third aspect, an electronic device is provided, including:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
determining the jumping degree of each node according to the value of each node corresponding to each user in the social network of the user; the value is determined by communication behavior data among all users;
when the value of the node with the maximum jumping degree in each node meets a preset condition, determining that a user corresponding to the node is an influential target person; the preset condition is that one or more parameters exceed a threshold value.
In a fourth aspect, a computer-readable storage medium is presented, the computer-readable storage medium storing one or more programs that, when executed by an electronic device that includes a plurality of application programs, cause the electronic device to:
determining the jumping degree of each node according to the value of each node corresponding to each user in the social network of the user; the value is determined by communication behavior data among all users;
when the value of the node with the maximum jumping degree in each node meets a preset condition, determining that a user corresponding to the node is an influential target person; the preset condition is that one or more parameters exceed a threshold value.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
according to the method, the value of each node corresponding to each user in the social network of the user is determined according to the data of communication behaviors among the users; determining the hop degree of each node according to the value of each node; and when the value of the node with the maximum jumping degree in each node meets a preset condition, determining that the user corresponding to the node is the target character with influence. Therefore, according to the method and the device, the target person with influence in the social network is determined based on the data of the communication behaviors among the users, the users and the social network are effectively analyzed, and the target person with influence in the social network is accurately analyzed.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart of a method for analyzing influence of a user in a social network according to an embodiment of the present disclosure;
fig. 2 is a diagram illustrating a layer of analysis of a social network structure in an actual application scenario of the method for analyzing influence of a user in a social network according to the embodiment of the present disclosure;
fig. 3 is a schematic flow diagram of user data in a social network user influence analysis method according to an embodiment of the present disclosure;
fig. 4 is a diagram of a first layer of a social network structure in an actual application scenario of the method for analyzing influence of a user in a social network according to the embodiment of the present application;
fig. 5 is a diagram of a two-layer social network structure in an actual application scenario of the method for analyzing influence of a user in a social network according to the embodiment of the present application;
fig. 6 is a three-layer social network structure diagram in an actual application scenario of the method for analyzing user influence in a social network according to the embodiment of the present application;
fig. 7 is a schematic view illustrating an opinion leader analysis of a three-layer social network structure in a practical application scenario of a method for analyzing influence of a user in a social network according to an embodiment of the present application;
fig. 8 is an active molecular analysis diagram of a three-layer social network structure in an actual application scenario of the user influence analysis method in the social network according to the embodiment of the present application;
fig. 9 is a schematic view illustrating an analysis of social flowers of a three-layer social network structure in an actual application scenario of the method for analyzing influence of a user in a social network according to the embodiment of the present application;
fig. 10 is a good idea analysis schematic diagram of a three-layer social network structure in an actual application scenario of the user influence analysis method in the social network according to the embodiment of the present application;
fig. 11 is a schematic diagram illustrating core character analysis of a three-layer social network structure in an actual application scenario of the method for analyzing influence of a user in a social network according to the embodiment of the present application;
fig. 12 is a schematic structural diagram of an apparatus for analyzing influence of a user in a social network according to an embodiment of the present disclosure;
fig. 13 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The method for analyzing the user influence in the social network provided by the embodiment of the application adopts an MVC (Model View Controller) frame, the MVC frame is a software design Model, codes are organized by a method of separating business logic, data and interface display, the business logic is gathered into one component, and the business logic does not need to be rewritten while the interface and the user interaction are improved and personalized. The MVC framework is used to map traditional input, processing, and output functions in the structure of a logical graphical user interface.
The execution subject of the method for analyzing the influence of the user in the social network provided by the embodiment of the application can be a computer, a server or a device/software for analyzing the influence of the user. In order to facilitate clear description of the method provided by the embodiment of the present application, the following takes an execution subject of the method as an example, and details of the method provided by the embodiment of the present application are described in detail.
Those skilled in the art will appreciate that the execution subject of the method is a server, which is only an exemplary illustration and is not a specific limitation of the execution subject of the method.
Fig. 1 shows a schematic flowchart of a method for analyzing influence of a user in a social network according to an embodiment of the present application, where as shown in fig. 1, the method includes:
step 101, determining the jumping degree of each node according to the value of each node corresponding to each user in the social network of the user.
The user's social network includes a plurality of nodes corresponding to the user, that is, the nodes in the social network represent the user. The social network may refer to a social group of a user, i.e., a circle of friends of a user.
In this step, the value is determined by communication behavior data between the respective users. The data of the communication behavior between the users can be analog data, such as voice calls and transmitted images between the users. The data of the communication behavior between the users may also be digital data, such as symbols and characters transmitted between the users. The data of the communication behavior between the users can be obtained from the communication records of the social application, such as the chat records of the QQ, the chat records of the WeChat, and so on.
The values may include in-degree, out-degree, mid-centrality, near-centrality, feature vector centrality for each node. The degree of entry can be the number of communication lines of the node for receiving communication information; the out-degree can be the number of communication lines for sending communication information by the node; the intermediate centrality may be the sum of the probabilities of a node being on a communication line between two nodes in the social network; the approach centrality may be the sum of the shortest communication lines between the node and other nodes in the social network; the feature vector centrality may be a centrality value vector of the node.
Before performing step 101, the following steps are performed: and acquiring the value of each node. Since the value may include the in-degree, out-degree, middle centrality, approximate centrality, and feature vector centrality of the node, the following method may be used to obtain the value of each node:
1) Acquiring the middle centrality of each node, and specifically adopting the following formula:
C ABi ＝∑∑b AB (i)；
b jk (i)＝g jk (i)/g jk
wherein, C ABi Representing the sum of the probabilities that the node i is positioned on a communication line between two nodes in the social network, wherein A and B represent the two nodes in the social network; b jk (i) Representing the probability of the node i on the communication line between the node j and the node k; k ≠ i and j<k；g jk Indicating the number of communication lines existing between the node j and the node k; g jk (i) Indicating the number of communication lines existing between node j and node k through node i.
2) Acquiring the approach centrality of each node, and specifically adopting the following formula:
wherein,representing that the sum of the shortest communication lines exists between the node i and other nodes in the social network; d ij Representing the sum of the communication lines between node i and node j.
The concept of recenterness of approach was proposed by scholars (a. Bavelas) et al, whose formal expression is given by sapidus (g. Sabidusi) (from Freeman, 1979).
3) The feature vector centrality of each node is obtained by specifically adopting the following formula:
x i ＝a 1i x 1 +a 2i x 2 +…+a ni x n ①
wherein x is i A vector of centrality values representing node i; a is ij Representing the contribution of node i to node j.
That is, the feature vector centrality refers to a centrality value of a node being a function of the centrality of other nodes selecting the node, i.e. the centrality of a node being a function of the centrality of other nodes selecting the node. Which is represented by adjacency matrix a.
A t Is the transpose of A in equation A t X = x (2) in formula (2), x is one feature vector (eigenvector) of a corresponding to the feature root 1. In general, equation (2) has no non-0 solution unless a has a characteristic root of 1. One way to solve these systems of equations is to normalize each row of the matrix so that the sum of each row is 1.At this time, equation (2) has a non-0 solution because A has a characteristic root of 1. Another method is to generalize equation (2) to become a feature vector equation in the general sense. We replace equations (1) and (2) with equations (3) and (4), respectively, λ x i ＝a 1i x 1 +a 2i x 2 ++a ni x n ③A t X = λ x (4) if a is an n × n matrix, equation (4) has n solutions corresponding to n λ values. The general form of the solution can be expressed in a matrix as a · X = X · λ. Where X is an n × n matrix, the columns of which are the n eigenvectors of matrix A, and λ is the diagonal matrix formed by the individual eigenvalues. The specific calculation procedure for the master signature root for a network's adjacency matrix is as follows (See Borgatti' Syllabus; bonacich, 1972): (1) At the beginning, the central value of each point is given as 1; (2) Recalculating the centrality of each point according to the weighted sum of the centrality values of all the neighbouring points of each point; (3) normalizing v by dividing each value by the maximum value; (4) Repeating steps (2) and (3) until the value of each v does not change.
After obtaining the value of each node, step 102 is executed to determine the hop degree of each node according to the value of each node, which may specifically adopt the following formula:
wherein, let X (1) ,X (2) …,X (n) Order statistics for sample capacity n from the population distribution F (x, θ);represents the hop degree of mu at the node K;to depend only on X (1) ,X (2) …,X (k) Node estimate of expected μ.
And 102, when the value of the node with the maximum jumping degree in each node meets a preset condition, determining that a user corresponding to the node is an influential target person.
The preset condition is that one or more parameters exceed a threshold. The threshold may be the largest of the 0.95 quantile degree values. For example, the in-degree of node a exceeds the in-degree of a node with a 0.95 quantile, the out-degree of node B exceeds the out-degree of a node with a 0.95 quantile, the intermediate centrality of node C exceeds the intermediate centrality of a node with a 0.95 quantile, the nearness centrality of node D exceeds the nearness centrality of a node with a 0.95 quantile, the feature vector centrality of node E exceeds the feature vector centrality of a node with a 0.95 quantile, and the total number of nodes A, B, C, D and E does not exceed the 0.05 quantile of the summary point.
In this step, if the degree of entry of a node meets a preset condition, it is determined that a user corresponding to the node is an opinion leader, and the opinion leader refers to a person who issues opinions and has many responses; if the out-degree of the node meets the preset condition, determining that the user corresponding to the node is an active molecule, wherein the active molecule is a person who frequently responds to opinions issued by other people; if the middle centrality of the node meets the preset condition, determining that the user corresponding to the node is an intersectant flower, wherein the intersectant flower is a person connecting two persons without direct contact; if the approach centrality of the node meets the preset condition, determining that the user corresponding to the node has good relationships, wherein the user with good relationships is a person with close contact with all users in the whole social network; if the feature vector centrality of the node meets a preset condition, determining that the user corresponding to the node is a core molecule, wherein the core molecule is a person with close contact with other key characters in the whole social network, and the other key characters can refer to opinion leaders, active molecules, social flowers and good edges.
In specific implementation, the value of each node in the social network of the user is determined according to the communication behavior data among the users. The social network may include at least the following users: 72. 25 and 62. Now taking users 72, 25 and 62 as examples, as shown in fig. 2, when a layer of social network structure is selected, and when the analyzed node corresponds to a common user, the first-layer neighbor node No. 25 of the user is an interstation flower, nodes No. 72 and No. 62 are not only core members but also good talents, and node No. 72 is also an opinion leader. The current node and the first-class friend circle of the node are not active in the whole social group, which shows that the active and other people are not in much contact. In practical application, assuming that a user corresponding to a current node is a suspect, preliminarily judging from the social structure, the suspect and a circle of friends thereof form a small group, wherein 25 belongs to an external contact of the group, and is presumed to be responsible for stealing, and 72 and 62 are relatively member persons in the group. The key characters can further analyze the network structure of the key characters by clicking the node links.
According to the method and the device, the degree value of each node in the social network of the user is determined through the communication behavior data among the users, so that the identity of the user corresponding to each node is determined, the structural analysis of a single user and a friend circle of the single user under the whole social network is realized, and the situation of the user related to the user can be known more clearly and intuitively. The analysis method can more prominently analyze the current analysis user as a core and analyze the user and the social network thereof.
Further, in order to filter out the circle of friends of a certain user, so as to analyze the user and the circle of friends of the user, before performing step 101, the method for analyzing influence of the user in the social network may further include the following steps: step 100, establishing a social network of the users according to the communication behavior data among the users. The user's social network includes a plurality of nodes corresponding to the user, that is, the nodes in the social network represent the user. The social network may refer to a social group of a user, i.e., a circle of friends of a user.
Further, in order to filter out the circle of friends in each level of a certain user, so as to analyze the user and the circle of friends in each level of the user, after step 100 is executed, the method for analyzing influence of the user in the social network may further include the following steps: and taking any node in the social network as a starting point, and searching for adjacent nodes adjacent to the node. The neighboring nodes adjacent to the node may refer to nodes which are spatially in the same hierarchy as the node and have no relation with the node, or nodes which have a relation with the node.
In this step, the searching for the neighboring node adjacent to the node may specifically be implemented in the following two ways:
the first search method may be referred to as breadth-first search, which is a traversal strategy for connecting social networks, and starts with a node to traverse a wider area around the node in a radial manner. The breadth-first search mode can be specifically realized as follows: and searching an Nth adjacent node which is communicated with the node by taking any node in the social network as a starting point, wherein N is a positive integer which is more than or equal to 1. And searching an Mth adjacent node which is communicated with the Nth adjacent node by taking the Nth adjacent node as a starting point, wherein M is a positive integer more than N and more than 1.
For example, with node V 0 For example, the breadth-first search mode is V 0 For the vertex, step S11, access node V 0 (ii) a Step S12, the slave node V 0 Departure access V 0 Each of the never-visited neighbor nodes W 1 ，W 2 ，…,W k (ii) a Step S13, the slave nodes W 1 ，W 2 ,…,W k Starting to access neighboring nodes which are not accessed; step S14, repeat step S12 until all vertices are visited.
The second search method may be called a depth-first search method, which is a traversal strategy for connecting social networks and traverses branches of a tree with a certain node as a starting point and along the depth of the tree. The depth-preferred search mode may be specifically implemented as follows: searching a No. P adjacent node which does not communicate with the node by taking any node in the social network as a starting point, wherein P is a positive integer which is more than or equal to 1; and (3) searching a Q-th adjacent node which does not have communication with the P-th adjacent node by taking the P-th adjacent node as a starting point, wherein Q is more than a positive integer of P & gt 1.
For example, with node V i For example, the deep preferred search pattern is V i As a vertex, step S21, visitNode V is asked i And marking the vertex; step S22, with V i Searching V for current vertex in sequence i Each neighbor node V of j If node V j If not, accessing and marking the adjacent node V j If V is j Has been visited, then search for node V i The next neighbor node of (2); step S23, with V j Repeating the step S22 for the current vertex until the node V in the interactive network i Accessing the vertexes with paths until all the vertexes are accessed; step S33, if there is a vertex that has not been visited yet in the interactive network (in case of non-connectivity), then one vertex that has not been visited in the graph can be taken as a starting point, and the above steps are repeated until all vertices in the interactive network are visited.
According to the method and the device, the social network structure of the user (the user and the neighbor thereof) is used for knowing which users have communication relations with the user. Moreover, the friend circle hierarchical relationship of the user corresponding to the node to be analyzed in the social network structure can be clearly seen.
Further, in order to visualize the social network, the method for analyzing the influence of the user in the social network may further include the following steps, as shown in fig. 3:
step S31, store the social network structure of the user in JS Object tagging (json) format on the host of the server.
The json format is a lightweight data exchange format that stores and represents data in a text format that is completely independent of the programming language based on a subset of the ECMAScript specification. The compact and clear hierarchy makes JSON an ideal data exchange language. The network transmission method is easy to read and write by people, is easy to analyze and generate by machines, and effectively improves the network transmission efficiency.
And step S32, importing the data of each user in the social network structure into a mysql database through a load tool.
MySQL is an open source relational database management system (RDBMS), among others.
And S33, performing visual display on the data stored in the mysql database.
The embodiment of the application realizes visual display and analysis of the user and the friend circle thereof, and the users who have communication relations with the user can be known through the social network structure of the user (the user and the neighbors thereof). Moreover, through different grouping methods, the identities of the users in the respective circle of friends can be known. Meanwhile, the social network structure of each user can be displayed on the basis of the social network structure.
When the method is specifically implemented, a social network of the users is established according to the communication behavior data among the users. The social network may include at least the following users: ID10002, ID72, ID39, ID14, ID95, ID77, ID5, and ID 51. Taking the user ID10002, ID72, ID39, ID14, ID95, ID77, ID5, and ID51 as an example, when a layer of social network structure is selected, clicking the current user ID10002 to visually display the user directly connected to the current user ID10002 (as shown in fig. 4) and the identity analysis information of the current user ID 10002; when the two-layer social network structure is selected, clicking the current user ID72, and visually displaying the adjacent user directly connected with the current user ID72, the user directly connected with the adjacent user (as shown in FIG. 5) and the identity analysis information of the current user ID 72; when a three-layer social network structure is selected, clicking a current user ID39, and visually displaying a first-level adjacent user directly connected with the current user ID39, a second-level adjacent user directly connected with the first-level adjacent user, a user directly connected with the second-level adjacent user (as shown in FIG. 6) and identity analysis information of the current user ID 39; when a three-layer social network structure is selected, clicking the current user ID14, visually displaying the identity analysis information of the current user ID14, and according to analysis, knowing that the current user ID14 is an opinion leader (as shown in FIG. 7); when a three-layer social network structure is selected, clicking the current user ID95, visually displaying the identity analysis information of the current user ID95, and according to analysis, knowing that the current user ID95 is an active molecule (as shown in FIG. 8); when a three-layer social network structure is selected, clicking the current user ID77, visually displaying the identity analysis information of the current user ID77, and knowing that the current user ID77 is an interstation flower according to analysis (as shown in FIG. 9); when a three-layer social network structure is selected, clicking the current user ID5, visually displaying the identity analysis information of the current user ID5, and according to the analysis, knowing that the current user ID5 is good (as shown in FIG. 10); when the three-layer social network structure is selected, the current user ID51 is clicked, the identity analysis information of the current user ID51 is visually displayed, and the current user ID51 is known as a core person according to analysis (as shown in fig. 11).
Fig. 12 is a schematic structural diagram of an apparatus for analyzing influence of a user in a social network according to an embodiment of the present disclosure, and as shown in fig. 12, the apparatus for analyzing influence of a user in a social network based on the same inventive concept as the method for analyzing influence of a user in a social network according to an embodiment of the present disclosure may include:
a hop degree determining module 121, configured to determine, according to a value of each node in the social network, a hop degree of each node; the value is determined by communication behavior data among all users;
a target person determining module 122, configured to determine that a user corresponding to a node with the maximum hop degree is a target person with influence when a value of a node with the maximum hop degree in each node meets a preset condition; the preset condition is that one or more parameters exceed a threshold value.
Wherein the threshold value is the largest value among the values of 0.95 quantiles.
The user influence analysis device in the social network may further include:
an obtaining module 123, configured to obtain a value of each node, where the value includes an in-degree, an out-degree, a middle centrality, an approximate centrality, and a feature vector centrality of each node;
the degree of entry is the number of communication lines for receiving communication information by the node; the out degree is the number of communication lines for the nodes to send communication information; the intermediate centrality is the sum of the probabilities that the nodes are positioned on the communication line between the two nodes in the social network; the approach centrality is the sum of the shortest communication lines between the node and other nodes in the social network; the feature vector centrality is the centrality value vector of the node.
The middle centrality of each node is obtained, and the following formula can be specifically adopted:
C ABi ＝∑∑b AB (i)
b jk (i)＝g jk (i)/g jk
wherein, C ABi Representing the sum of the probabilities that a node i is positioned on a communication line between two nodes in the social network, wherein A and B represent the two nodes in the social network; b jk (i) Representing the probability of the node i on the communication line between the node j and the node k; k ≠ i and j<k；g jk Indicating the number of communication lines existing between the node j and the node k; g is a radical of formula jk (i) Indicating the number of communication lines existing between node j and node k through node i.
The approach centrality of each node is obtained by using the following formula:
wherein,the sum of the shortest communication lines between the node i and other nodes in the social network is represented; d is a radical of ij Representing the sum of the communication lines between node i and node j.
The feature vector centrality of each node is obtained by specifically adopting the following formula:
x i ＝a 1i x 1 +a 2i x 2 +…+a ni x n
wherein x is i A vector of centrality values representing node i; a is ij Representing the contribution of the position of node i to node j.
Determining the hop degree of each node, which may specifically adopt the following formula:
wherein,represents the hop degree of mu at the node K;to depend only on X (1) ,X (2) …,X (k) Node estimate of expected μ.
The user influence analysis device in the social network may further include:
the searching module 124 is configured to search for an adjacent node adjacent to a node, starting from any node in the social network.
The lookup module 124 may include:
the first searching unit is used for searching an Nth adjacent node which is communicated with the node by taking any node in the social network as a starting point, wherein N is a positive integer which is more than or equal to 1;
and the second searching unit is used for searching an Mth adjacent node which is communicated with the Nth adjacent node by taking the Nth adjacent node as a starting point, wherein M is a positive integer larger than N and larger than 1.
The lookup module may include:
the third searching unit is used for searching a pth adjacent node which does not communicate with the node by taking any node in the social network as a starting point, wherein P is a positive integer which is more than or equal to 1;
and the fourth searching unit is used for searching a Q-th adjacent node which does not have communication with the P-th adjacent node by taking the P-th adjacent node as a starting point, wherein Q is more than a positive integer of P more than 1.
The user influence analysis device in the social network may further include:
the establishing module 125 is configured to establish a social network according to communication behavior data between users; the social network comprises a plurality of nodes corresponding to the user.
The method comprises the steps that according to data of communication behaviors among users, the value of each node in the social network is determined; determining the hop degree of each node according to the value of each node; and when the value of the node with the maximum jumping degree in each node meets a preset condition, determining that the user corresponding to the node is the target character with influence. Therefore, according to the method and the device, the target person with influence in the social network is determined based on the data of the communication behaviors among the users, the users and the social network are effectively analyzed, and the target person with influence in the social network is accurately analyzed.
FIG. 13 is a schematic structural diagram of user influence analysis in a social network according to an embodiment of the present application. Referring to fig. 13, at a hardware level, the user influence analysis apparatus in the social network includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the means for multi-player game resource allocation may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 7, but this does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the nonvolatile memory into the memory and runs the computer program to form the batch processing device applying the big data on a logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
determining the hop degree of each node according to the value of each node in the social network; the value is determined by communication behavior data among all users;
when the value of the node with the maximum jumping degree in each node meets a preset condition, determining that a user corresponding to the node is an influential target person; the preset condition is that one or more parameters exceed a threshold value.
The method performed by the user influence analysis device in the social network according to the embodiment shown in fig. 1 of the present application may be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The user influence analysis device in the social network may also execute the method for allocating multiplayer game resources in fig. 1, and implement the functions of the user influence analysis device in the social network in the embodiment shown in fig. 1, which are not described herein again in this embodiment of the present application.
An embodiment of the present application further provides a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which when executed by an electronic device including a plurality of application programs, enable the electronic device to perform the method for analyzing influence of a user in a social network in the embodiment shown in fig. 1, and are specifically configured to perform:
determining the hop degree of each node according to the value of each node in the social network; the value is determined by communication behavior data among all users;
when the value of the node with the maximum jumping degree in each node meets a preset condition, determining that a user corresponding to the node is an influential target person; the preset condition is that one or more parameters exceed a threshold value.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as an apparatus, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (20)
1. A method for analyzing influence of users in a social network, the method comprising:
determining the hop degree of each node corresponding to each user according to the value of each node in the social network of the user; the value is determined by communication behavior data among all users;
when the value of the node with the maximum jumping degree in each node meets a preset condition, determining that a user corresponding to the node is an influential target person; the preset condition is that one or more parameters exceed a threshold value.
2. The method of claim 1, further comprising, prior to determining the hop-count for each of the nodes:
obtaining the value of each node, wherein the value comprises the degree of entry, the degree of exit, the middle centrality, the degree of approach centrality and the feature vector centrality of each node;
the degree of entry is the number of communication lines of the node for receiving communication information; the out degree is the number of communication lines for the nodes to send communication information; the intermediate centrality is the sum of the probabilities that the node is located on a communication line between two nodes in the social network; the approach centrality is the sum of the shortest communication lines between the node and other nodes in the social network; the feature vector centrality is a centrality value vector of the node.
3. The method according to claim 2, wherein the obtaining of the intermediate centrality of each node specifically adopts the following formula:
C ABi ＝∑∑b AB (i)
b jk (i)＝g jk (i)/g jk
wherein, C ABi Representing the sum of the probabilities that node i is on the communication line between two nodes in the social network, and A and B represent two nodes in the social networkPoint; b jk (i) Representing the probability of the node i on the communication line between the node j and the node k; k ≠ i and j<k；g jk Indicating the number of communication lines existing between the node j and the node k; g jk (i) Indicating the number of communication lines existing between node j and node k through node i.
4. The method according to claim 2, wherein the approximate centrality of each node is obtained by using the following formula:
wherein,representing that the sum of the shortest communication lines exists between the node i and other nodes in the social network; d ij Representing the sum of the communication lines between node i and node j.
5. The method according to claim 2, wherein the obtaining of the feature vector centrality of each node specifically adopts the following formula:
x i ＝a 1i x 1 +a 2i x 2 +…+a ni x n
wherein x is i A vector of centrality values representing node i; a is ij Representing the contribution of node i to node j.
6. The method according to claim 2, wherein the determining the hop count of each node specifically adopts the following formula:
wherein,represents the hop degree of mu at the node K;to depend only on X (1) ,X (2) …,X (k) Node estimate of expected μ.
7. The method of claim 2, wherein the threshold is the largest of the 0.95 quantile degree values.
8. The method of claim 1, further comprising, prior to determining the hop-count for each of the nodes:
and establishing a social network of the users according to the communication behavior data among the users.
9. The method of claim 8, further comprising, after establishing the social network of the user:
and taking any node in the social network as a starting point, and searching for an adjacent node adjacent to the node.
10. The method according to claim 9, wherein searching for neighboring nodes adjacent to the node specifically comprises:
searching an Nth adjacent node which is communicated with the node by taking any node in the social network as a starting point, wherein N is a positive integer which is more than or equal to 1;
and searching an Mth adjacent node which is communicated with the Nth adjacent node by taking the Nth adjacent node as a starting point, wherein M is a positive integer more than N and more than 1.
11. The method according to claim 9, wherein searching for neighboring nodes adjacent to the node specifically comprises:
searching a No. P adjacent node which does not have communication with the node by taking any node in the social network as a starting point, wherein P is a positive integer larger than or equal to 1;
and searching a Q-th adjacent node which does not have communication with the P-th adjacent node by taking the P-th adjacent node as a starting point, wherein Q is a positive integer more than P & gt 1.
12. An apparatus for analyzing influence of a user in a social network, the apparatus comprising:
the jumping degree determining module is used for determining the jumping degree of each node according to the value of each node corresponding to each user in the social network of the user; the value is determined by communication behavior data among all users;
the target person determining module is used for determining that the user corresponding to the node is the target person with influence when the value of the node with the maximum jumping degree in each node meets a preset condition; the preset condition is that one or more parameters exceed a threshold value.
13. The apparatus of claim 12, further comprising:
the acquisition module is used for acquiring the value of each node, wherein the value comprises the degree of entry, the degree of exit, the middle centrality, the approximate centrality and the feature vector centrality of each node;
the degree of entry is the number of communication lines of the node for receiving communication information; the out degree is the number of communication lines for the nodes to send communication information; the intermediate centrality is the sum of the probabilities that the node is located on a communication line between two nodes in the social network; the approach centrality is the sum of the shortest communication lines between the node and other nodes in the social network; the feature vector centrality is a centrality value vector of the node.
14. The apparatus of claim 13, wherein the threshold is a largest of the 0.95 quantile degree values.
15. The apparatus of claim 12, further comprising:
and the establishing module is used for establishing a social network according to the communication behavior data among the users.
16. The apparatus of claim 15, further comprising:
and the searching module is used for searching the adjacent nodes adjacent to the nodes by taking any node in the social network as a starting point.
17. The apparatus of claim 16, wherein the lookup module comprises:
the first searching unit is used for searching an Nth adjacent node which is communicated with the node by taking any node in the social network as a starting point, wherein N is a positive integer which is more than or equal to 1;
and the second searching unit is used for searching an Mth adjacent node which is communicated with the Nth adjacent node by taking the Nth adjacent node as a starting point, wherein M is a positive integer larger than N and larger than 1.
18. The apparatus of claim 16, wherein the lookup module comprises:
a third searching unit, configured to search, using any node in the social network as a starting point, a pth adjacent node that does not communicate with the node, where P is a positive integer greater than or equal to 1;
and the fourth searching unit is used for searching a Q-th adjacent node which does not have communication with the P-th adjacent node by taking the P-th adjacent node as a starting point, wherein Q is a positive integer greater than P & gt 1.
19. An electronic device, comprising:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to:
determining the hop degree of each node according to the value of each node in the social network; the value is determined by communication behavior data among all users;
when the value of the node with the maximum jumping degree in each node meets a preset condition, determining that a user corresponding to the node is an influential target person; the preset condition is that one or more parameters exceed a threshold value.
20. A computer-readable storage medium storing one or more programs that, when executed by an electronic device including a plurality of application programs, cause the electronic device to:
determining the hop degree of each node according to the value of each node in the social network; the value is determined by communication behavior data among all users;
when the value of the node with the maximum jumping degree in each node meets a preset condition, determining that a user corresponding to the node is an influential target person; the preset condition is that one or more parameters exceed a threshold value.
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