Node-activity-based asymmetrical cell group expansion overlapping cell discovering and predicting method in complex network

Abstract

The invention aims to the research on the discovery of overlapping cell in the evolution of a complex network, which does not consider the dynamic evolution, the uneven activity distribution and the multi-scaling of individuals so that the overlapping cell could not improve the NMI (Normalized Mutual Information) and the F-score. Therefore, the method discloses a node-activity-based asymmetrical cell group expansion overlapping cell discovering and predicting method, comprising: first, defining the activity of the nodes; using the activity of the nodes on the objective function for overlapping cell expansion; then, expanding the cell and using the biggest cell group as the expanding seed; and finally, working out the asymmetrical cell group expansion algorithm based on the node activity. According to the invention, it is possible to radically increase the detection accuracy for the overlapping cell in a complex network and to possess a better predicting capability for an overlapping community in the future.

Description

Technical field [0001] The invention relates to the field of complex networks, in particular to a method for discovering and predicting overlapping communities based on asymmetric community expansion based on node activity in complex networks. Background technique [0002] In 2005, Palla et al. proposed the definition of allowing nodes to belong to multiple communities at the same time, that is, allowing communities to overlap. They promoted traditional community discovery problems to overlapping community discovery. In order to obtain overlapping communities, Palla et al. proposed the CPM algorithm (CPM, CliquePercolation Method) based on the nature that the nodes in the community are connected to most other nodes in the community without connecting to all nodes. In the CPM algorithm, first search for a structure called k-clique (a complete subgraph containing at least k nodes and not a subset of any other complete subgraph), and then search for adjacent k-clique (shared k-cliq...

AI Technical Summary

Problems solved by technology

[0013] Existing research is mainly based on methods such as expansion, density, hierarchical clustering, and statistical inference to realize overlapping community discovery, which makes the detection accuracy of overlapping community detection and analysis methods low, the error rate of overlapping node assignment is high, and the future evolution of overlapping communities cannot be predicted. Defects, the lack of a method system for the evolution analysis of overlapping communities in a dynamic network environment has become a bottleneck hindering the development of related theories and applications of overlapping communities in complex networks

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