Multi-label propagation discovery method of overlapping communities in social network

Abstract

The invention relates to the technical field of a social network and particularly relates to a multi-label propagation discovery method of overlapping communities in the social network. The multi-label propagation discovery method comprises the following steps: reading data of the social network, constructing a social network diagram which adopts social network users as nodes and user relationship as edges; according to the social network diagram, carrying out preliminary community division of the social network, and carry outing community discovery by adopting a label propagation method of comprehensively considering the node centrality and label-degree distribution constraint to obtain a non-overlapping community structure; marking the levels of the nodes according to the obtained non-overlapping community structure and the centrality value of the nodes in the communities; and according to the levels of the nodes, calculating label propagation gain among the nodes with different levels, and carrying out overlapping node mining by utilizing the multi-label propagation to obtain the overlapping community structure of the social network. The multi-label propagation discovery method has the advantages that the overlapping community structure in the social network can be effectively mined, the accuracy and the efficiency of community detection are favorably improved, and the method can be applicable to the fields of target group mining, precision marketing and the like.

Description

technical field [0001] The invention relates to the technical field of social networks, in particular to a method for discovering overlapping communities of multi-label propagation in social networks. Background technique [0002] Detecting community structure from social networks is an important task in social network analysis, which is of great significance both in theory and in practical applications. By mining the community structure in the network, we can discover the hidden organizational structure information, social functions and interesting attributes among community members, such as common hobbies, etc. in the network. By studying the relationships between communities, individuals, and individuals and communities in social networks, a large amount of valuable information can be mined, which can be applied in many fields. [0003] For community discovery, many classic methods have emerged. In 2002, Girvan and Newman proposed the GN method based on the edge between...

AI Technical Summary

Problems solved by technology

In the face of large-scale social network scenarios, existing methods are difficult to meet the requirements in terms of actual effect and efficiency

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