CN111127233A - User check value calculation method in undirected authorized graph of social network - Google Patents
User check value calculation method in undirected authorized graph of social network Download PDFInfo
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Abstract
The invention discloses a user check value calculation method in a directed authorized graph of a social network, and belongs to the field of social networks. The method comprises the following steps: s1, calculating the sum of the weights of all neighbor nodes of each user node in the undirected weighted graph of the social network, and initializing k to be 0; s2, judging whether the user nodes in the graph are all deleted, if so, ending, otherwise, entering S3; s3, finding out a user node u with the lowest sum of all neighbor node weights in the graph; s4, if the sum cd of the weights of all the neighbor nodes of u is greater than k, the kernel value of u is cd, the value of k is updated to the kernel value of u, and otherwise, the kernel value of u is k; s5, deleting u and all edges connected with u from the graph, recalculating the sum of all neighbor node weights of each user node, and entering the step S2. The method measures the influence of the social network of the user node by using the sum of the weights of all the neighbor nodes, iteratively subtracts the node with the minimum sum of the weights of all the neighbor nodes until the calculation of the core value is completed, and is suitable for the undirected weighted graph of the social network.
Description
Technical Field
The invention belongs to the field of social networks, and particularly relates to a user accounting value calculation method in an undirected authorized graph of a social network.
Background
Social network node influence research is one of the key issues in social network analysis. The topology of the social network, the user interaction behavior, and the user content constitute 3 elements of the social network. The topological structure can depict the influence of the nodes on a macroscopic level, and is easy to obtain, and the topological structure indexes in a complex network are relatively mature, so that the method for measuring the influence of the nodes by using the topological structure becomes a common method. Topology-based metrics can be divided into: 4 types of the method are based on local attributes, global attributes, random walks and community relations. The node influence measurement indexes based on the node global attributes mainly consider the global network information of the network where the nodes are located, the indexes can better reflect the topological characteristics of the nodes, but the time complexity is high, and most indexes are not suitable for large-scale networks. Kitsak et al found that the high betweenness or Hubs nodes are not necessarily the most influential nodes through empirical studies on social networks, mail networks and the like, and the nodes are divided into different levels from the edge layer to the core layer in position by using K-kernel decomposition, and the core node (the node with a large Ks value) is considered to be the node with a large influential value. As shown in fig. 1, the nodes are divided into 3 layers by iteratively subtracting nodes with degrees less than or equal to K, wherein the node with Ks of 3 belongs to the core node, i.e. the node with large influence. The node with the Ks value of 1 belongs to the edge layer and has small influence.
The proposal of K-nucleus decomposition brings great inspiration for the majority of researchers. In recent years, a plurality of scholars improve some defects of K-nucleus decomposition, and further improve the accuracy and the application range of the K-nucleus decomposition. However, most of the existing methods only focus on the unwarranted graph, and the proposed kernel value calculation method is not suitable for the authorized graph. Only the number of edges is considered in the non-weighted graph, and the weighted graph also needs to consider the weight of the point. However, the ownership graph is common in the real world, for example, in the social network ownership graph, different individuals have weights in the social network, and the weights reflect different degrees of influence.
Disclosure of Invention
Aiming at the defects and the improvement requirement of the prior art, the invention provides a user check value calculation method in an undirected authoritative graph of a social network, and aims to provide a check value calculation method suitable for the authoritative graph.
To achieve the above object, according to a first aspect of the present invention, there is provided a method for computing user check values in a social network undirected weighted graph, the method comprising the steps of:
s1, calculating the sum of the weights of all neighbor nodes of each user node in the undirected weighted graph of the social network, and initializing a k value to be 0;
s2, judging whether all user nodes in the undirected authorized graph of the social network are deleted, if so, finishing the check value calculation, otherwise, entering a step S3;
s3, finding out a user node u with the lowest sum of all neighbor node weights in the undirected weighted graph of the social network;
s4, if the sum cd of all neighbor node weights of the user node u is greater than k, the kernel value of the user node u is cd, the value of k is updated to the kernel value of the user node u, and otherwise, the kernel value of the user node u is k;
s5, deleting the user node u and all edges connected with the user node u from the undirected weighted graph of the social network, recalculating the sum of the weights of all neighbor nodes of each user node, and entering the step S2.
Preferably, the social network undirected ownership graph is obtained as follows:
the user abstraction in the social network is a node, the relationship between the users is abstracted as an edge, the influence of the users in the social network is abstracted as a weight, and an undirected weighted graph G { V, E, W } of the social network is obtained, wherein V is a node set, E is an edge set, and W is a weight set.
Preferably, when the social network is a microblog social network, the weight is the number of fans of the user.
Preferably, the greater the calculated user score, the more important the user is in the social network.
Preferably, the method is applied to advertisement putting.
Preferably, the method is applied in preventing the propagation of harmful information.
To achieve the above object, according to a second aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for computing user check values in a social network undirected authoritative graph as set forth in the first aspect.
Generally, by the above technical solution conceived by the present invention, the following beneficial effects can be obtained:
aiming at the problem that the number of edges is only considered in the computation of the weightless graph core value of the social network in the prior art, the invention measures the influence of the social network of the user node by using the sum of the weights of all the neighbor nodes of the user node, and leads the core value computation to be suitable for the undirected weighted graph of the social network by iteratively subtracting the node with the minimum sum of the weights of all the neighbor nodes until the computation of the core value of all the user nodes is completed, thereby conforming to the practical situation.
Drawings
FIG. 1 is a schematic diagram of a prior art K-nucleus decomposition;
FIG. 2 is a flowchart of a method for calculating a user check value in an undirected ownership graph of a social network according to the embodiment;
fig. 3 is a schematic diagram of the calculation of the kernel value provided in this embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in FIG. 2, the present invention provides a method for computing user check value in a directed ownership graph of a social network, which comprises the following steps:
s1, calculating the sum of all neighbor node weights of each user node in the undirected weighted graph of the social network, and initializing a k value to be 0.
Converting the social network into an undirected weighted graph G ═ V, E, W }, wherein nodes in the node set V represent users in the social network, e.g., microblog users; edges in the edge set E represent a relationship between two users, e.g., an attention relationship; the weights in the set of weights W represent the user's influence in the social network, e.g., number of fans.
And calculating the weighted value of each user according to the weight of the individual in the social network, wherein the weighted value is used as a judgment index of the importance of the individual in the weighted graph.
And S2, judging whether all the user nodes in the undirected authorized graph of the social network are deleted, if so, finishing the calculation of the check value, otherwise, entering the step S3.
And deleting the users in the social network, which shows that the calculation of the core value is completed, and each user obtains the corresponding core value. The larger the user's kernel value, the greater the user's influence. Further applications, for example, in advertisement delivery, a user with high core value and high influence is allowed to publish advertisement information in a social network, and the advertisement information can be more widely spread in the social network. In preventing the spread of harmful information (such as rumors), users with larger core values in the social network are prevented from continuously spreading the information in time, and the spread of the information can be terminated quickly.
And S3, finding out a user node u with the lowest sum of all neighbor node weights in the undirected weighted graph of the social network.
In the microblog social network, if the number of fans and the value of all connected users of the user node are the lowest, the influence of the user in the current rest social network is the lowest, and a core value can be allocated to the point.
And S4, if the sum cd of the weights of all the neighbor nodes of the user node u is greater than k, the kernel value of the user node u is cd, the value of k is updated to the kernel value of the user node u, and otherwise, the kernel value of the user node u is k.
The sum cd of the weights of all the neighbor nodes of the user node u is greater than k, which indicates that u has an influence greater than that of the last point to which the kernel value has been allocated after the node and the edge are removed in the previous step, and the user can be allocated with a new kernel value because the user is the user with the lowest influence in the remaining networks.
And S5, deleting the user node u and all edges connected with the user node u from the undirected weighted graph of the social network, recalculating the weight sum of all neighbor nodes of each user node, and entering the step S2.
In the first embodiment, the undirected ownership graph of the social network is shown in fig. 3, and the process of computing the check value is specifically as follows;
and calculating the fan number sum of the users connected with each user. At this time, the number of fans and the lowest node V5Its kernel value is updated to 7, k is 7, and user V is updated5And<V5，V3>，<V5，V4>removed from the figure.
Calculating the fan number sum of the users connected with each user after removing the user, wherein the fan number sum at the moment is the user V4The value is 3, but 3 < 7, so V4Updates the kernel value of (b) to 7, k is unchanged, and updates V4And<V4，V3>removed from the figure.
Calculating the fan number sum of the users connected with each user after removing the user, wherein the fan number sum at the moment is the user V3The value is 3, 3 < 7, so V3Updates the kernel value of (b) to 7, k is unchanged, and updates V3And<V3，V1>removed from the figure.
Calculating the fan number sum of the users connected with each user after removing the user, wherein the fan number sum at the moment is the user V2The value is 9, 9 > 7, so V2Updates the kernel value of 9, k is 9, and updates V2And<V2，V1>and<V2，V8>removed from the figure.
The users and the corresponding contacts (edges) are thus gradually removed from the graph until the kernel value for each user, which is indicated by the grey numbers next to each node in fig. 3, is successfully calculated after all users have been removed from the network.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (7)
1. A method for computing user check value in a directed ownership graph of a social network is characterized by comprising the following steps:
s1, calculating the sum of the weights of all neighbor nodes of each user node in the undirected weighted graph of the social network, and initializing a k value to be 0;
s2, judging whether all user nodes in the undirected authorized graph of the social network are deleted, if so, finishing the check value calculation, otherwise, entering a step S3;
s3, finding out a user node u with the lowest sum of all neighbor node weights in the undirected weighted graph of the social network;
s4, if the sum cd of all neighbor node weights of the user node u is greater than k, the kernel value of the user node u is cd, the value of k is updated to the kernel value of the user node u, and otherwise, the kernel value of the user node u is k;
s5, deleting the user node u and all edges connected with the user node u from the undirected weighted graph of the social network, recalculating the sum of the weights of all neighbor nodes of each user node, and entering the step S2.
2. The method of claim 1, wherein the social network undirected ownership graph is obtained as follows:
the user abstraction in the social network is a node, the relationship between the users is abstracted as an edge, the influence of the users in the social network is abstracted as a weight, and an undirected weighted graph G { V, E, W } of the social network is obtained, wherein V is a node set, E is an edge set, and W is a weight set.
3. The method of claim 1 or 2, wherein the weight is the number of fans of the user when the social network is a microblog social network.
4. The method of any of claims 1 to 3, wherein the greater the computed user score, the more important the user is in the social network.
5. The method of any one of claims 1 to 4, wherein the method is applied in advertisement placement.
6. A method according to any one of claims 1 to 4, characterized in that the method is applied in preventing the propagation of harmful information.
7. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, implements a method for computing user check values in a social network undirected weighted graph as claimed in any one of claims 1 to 6.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112380267A (en) * | 2020-10-21 | 2021-02-19 | 山东大学 | Community discovery method based on privacy graph |
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| CN113158025A (en) * | 2021-03-05 | 2021-07-23 | 腾讯科技（深圳）有限公司 | Method and device for generating product release strategy and electronic equipment |
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| CN114297572B (en) * | 2022-01-06 | 2022-11-29 | 中国人民解放军国防科技大学 | Method and device for identifying node propagation influence in social network and computer equipment |
| CN116436799A (en) * | 2023-06-13 | 2023-07-14 | 中国人民解放军国防科技大学 | Complex network node importance assessment method, device, equipment and storage medium |
| CN116436799B (en) * | 2023-06-13 | 2023-08-11 | 中国人民解放军国防科技大学 | Complex network node importance assessment method, device, equipment and storage medium |
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