Network community detection method based on M elite coevolution strategy

Abstract

The invention discloses a network community detection method based on an M elite coevolution strategy, wherein the network community detection method solves the problems that in the prior art, the convergence rate is low and easily lapses into the local optimum, multiresolution analysis of a network structure cannot be achieved. The implementation steps include that (1) network data are loaded; (2) network community populations are initialized; (3) the network community populations are divided; (4) a network community team is organized; (5) candidate network community division is detected; (6) the network community populations are updated; (7) local network communities are detected; (8) the network community populations are updated; (9) whether iteration is terminated or not is judged; (10) a network community detection result is output. When the network community detection method is used for detecting community structures in a network, expanded module density functions serve as fitness functions, a network structure is analyzed with different resolutions, and the convergence rate is quickened through leading-in of local detection and does not easily lapse into the local optimum.

Description

technical field [0001] The invention belongs to the field of computer network technology, and further relates to a community network detection method based on M elite cooperative evolution strategy in the field of artificial intelligence technology. The invention uses the expanded module density function as the fitness function and introduces the M-elite co-evolution algorithm to find community structures with different resolutions in the real network, which has high convergence speed and stability. The invention can be used to solve the problem of community structure detection in the network. Background technique [0002] Many complex systems in the real world can be represented as networks, such as the World Wide Web, power grids, biological networks, and social networks. In addition to network properties such as small-world effects and scale-free, community structure is another important property in complex network structures. Community refers to a collection of nodes w...

AI Technical Summary

Problems solved by technology

The present invention uses the expanded module density function as the fitness function and introduces the simulated annealing method as a local detection strategy to solve the shortcomings of the existing network community structure detection methods, such as resolution limitation, poor robustness, and easy to fall into local optimum, etc. Accuracy of Online Community Detection

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