Self-adaptive overlapping community discovery method and system based on density peak value

Abstract

The invention discloses a self-adaptive overlapping community discovery method based on a density peak value. The method comprises the following steps: S1, calculating side link strength and point link strength between nodes based on an adjacent matrix of a node network; S2, constructing a distance matrix of the node network, and calculating the local density rho and the shortest distance delta of each node based on the distance matrix; S3, adaptively selecting a community center in the network through the local density rho and the shortest distance delta of the nodes; and S4, based on the probability vectors of the remaining nodes, allocating the remaining nodes to the communities corresponding to the at least one community center, and determining overlapping communities according to the allocated community set. The density peak value-based adaptive overlapping community discovery method provided by the invention is low in complexity, good in overlapping community discovery effect and better in result of dividing a large-scale complex network.

Description

technical field [0001] The invention belongs to the technical field of computer and communication, and more specifically, the invention relates to a density peak-based self-adaptive overlapping community discovery method and system. Background technique [0002] In real life, many complex systems can be modeled as a complex network for analysis, such as common kinship networks, aviation networks, transportation networks, computer networks, literature citation networks, and social networks. Community discovery divides the community structure according to the degree of connection between network nodes, and classifies the nodes in the network reasonably. Community overlap is an important feature in the network, that is, there are overlapping nodes between different communities. The detection of overlapping communities has important research value and scientific significance for network structure analysis and community division. [0003] Existing community detection algorithms...

AI Technical Summary

Problems solved by technology

[0004] Most overlapping community discovery algorithms using density peaks have the disadvantage of not being able to accurately calculate the distance between nodes, because most algorithms do not consider the influence of edges and common nodes on distance calculation; or they cannot adaptively select the community center, because the calculated After the node density and the shortest distance, it is necessary to subjectively select the community center from the decision-making graph, which is highly subjective; or due to the high complexity of the algorithm, it leads to shortcomings such as low detection efficiency, long time, and inability to detect large-scale complex networks.

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