Community discovery method and system

Abstract

The invention provides a community discovery method. The community discovery method includes the steps that community division is conducted on a plurality of nodes in a network based on modularity maximization, and community boundary nodes obtained in the last step are adjusted based on community property entropy minimality. The community discovery method further includes the steps that if the community division obtained after adjustment meets an end condition, the community division will be used as final community division; otherwise, the communities obtained after adjustment will be used as nodes, community division will be conducted on the nodes again, and the community boundary nodes need to be readjusted. According to the community discovery method, the structure of the network and the attributive characters of the nodes are taken into account at the same time, and the degree of accuracy of community discovery is improved. In addition, the community discovery method is close to be linear in time complexity and suitable for large-scale on-line social network data.

Description

technical field [0001] The invention relates to the fields of complex network analysis and data mining, in particular to a community discovery method and system. Background technique [0002] With the in-depth study of the nature and mathematical characteristics of social networks, researchers have found that many networks have a common feature - community structure, that is to say, the network is composed of several "groups" or "groups", each The connections between nodes in a "cluster" are very tight, while the connections between "clusters" are relatively sparse. The discovery of network communities can help people understand the structural characteristics of the network more effectively, so as to provide more effective and personalized services. For example: for information recommendation, user classification, and Internet group behavior analysis. On online social networks, the self-media nature of individuals often produces a large amount of text content, which reflec...

AI Technical Summary

Problems solved by technology

In addition, although many existing graph clustering methods have combined the structure of the network and the attribute characteristics of nodes (or node attributes or node attribute information) (for example, constructing new graph clustering methods by weighting attributes and structures) network, and perform community division on the new network), but the results of these clusters often have communities that are not structurally close or not related, which leads to inaccurate results of community discovery; moreover, the time complexity of these methods High, not suitable for processing large-scale data

Images