Local shortest loop based social network group division method

Abstract

The invention relates to a local shortest loop based social network group division method. The method specifically comprises the steps of reading social network data, and constructing a social network graph taking social network users as nodes and user relationships as edges; endowing each user with a unique tag value representing a community that the user belongs to; and updating tags of user nodes by adopting a local shortest loop based tag propagation algorithm to obtain a community structure of a social network, wherein the closely relevant nodes have the same tag values through iterative updating for multiple times. According to the social network group division method provided by an embodiment of the invention, a tag propagation algorithm is improved with an attribute whether a local loop is constructed or not in a user relationship graph; and compared with an existing community network division method, the division of user groups is favorable for improving the accuracy of community division, the community structure in the social network is effectively mined, and division results have relatively high application values in the fields of network public opinion monitoring, search engines, commercial target mining and the like.

Description

technical field [0001] The invention relates to the technical field of social network computing, in particular to a social network group division method based on local shortest loops. Background technique [0002] Most complex networks exhibit modular characteristics, that is, there are some relatively independent groups in the system. This group of densely connected internal nodes and sparsely connected with other parts of the network is called a community. Generally speaking, there is a certain degree of similarity between nodes in the community, and this similarity is of great significance for people to study complex networks. For example, people in the same group in a social network have common characteristics or opinions that are different from other groups, which is of great significance to the monitoring of network public opinion; on the World Wide Web, if you know a small amount of information on certain web pages, you can extrapolate it to other groups in the same ...

AI Technical Summary

Problems solved by technology

[0005] Although the traditional label propagation algorithm has low time complexity and fast speed, in the process of label propagation, the algorithm believes that each neighbor node of the updated node has an equal impact on the node, and the connection relationship between the neighbor node and the node If it is not taken into account, it can easily lead to the arbitrary spread of labels among different communities, which in turn affects the accuracy of the label propagation algorithm.

[0006] In view of the above shortcomings, there is still a lot of room for improvement in the accuracy of the division results of existing community detection methods

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