Network community discovery method

Abstract

The invention discloses a network community discovery method, which is used for community structure discovery in complex network analysis. The entire community discovery process of the present invention is divided into two stages. In the first stage, the node subgraph of each node in the network is used to divide the community into the community; in the second stage, based on the node subgraph The community partition results determine the order of node label propagation and update rules of each node community label. When propagating and updating the community labels of nodes in the network, the propagation order is no longer random, but the information entropy of nodes is used to measure the amount of information of network nodes, and the labels are propagated in the order of information entropy from small to large, so that Avoid the reduction of accuracy caused by randomness and uncertainty; at the same time, the update principle of node labels is no longer simply dependent on the frequency of labels, but comprehensively evaluates the uncertainty of node labels using optimal modularity and information entropy , thus avoiding the blindness of label updating.

Description

technical field [0001] The invention relates to the technical field of complex networks, in particular to a network community discovery method. Background technique [0002] A complex network generally refers to a network with a large number of nodes and complex connections. There are a large number of complex networks in nature. The nodes in the network are abstracted from various entities, and the relationship between entities constitutes the edges in the network. For example, social networks, Internet networks, protein networks, economic and financial networks, and scientific research and education network, etc. Complex networks have flexible and universal characterization capabilities, and can be widely used in various disciplines to model and analyze complex systems. Therefore, the study of complex networks has become a very challenging topic. The uneven distribution of links in a complex network implies the existence of a community structure, and more and more studie...

AI Technical Summary

Problems solved by technology

[0008] Based on the modularity optimization model and information entropy theory, the present invention solves the problem that the accuracy of the tag propagation community discovery method is reduced due to uncertainty and randomness, and at the same time controls the computational complexity at a lower level, which is a community of large-scale and complex networks. Discovery provides a way to balance discovery accuracy with computational complexity

Images