Evolutionary multi-objective optimization community detection method based on affinity propagation

Abstract

The invention relates to an evolutionary multi-objective optimization community detection method based on affinity propagation. The method is characterized by comprising the first step of starting the evolutionary multi-objective optimization community detection method based on affinity propagation, the second step of reading in an adjacent matrix of a network, the third step of setting correlation parameters, the fourth step of selecting a similarity measuring method based on signals, the fifth step of selecting the clustering method of affinity propagation, the sixth step of selecting a multi-objective evolutionary algorithm, the seventh step of obtaining a preliminary partition result based on the fifth step and obtaining a further partition result based on the sixth step, and the eighth step of finishing the evolutionary multi-objective optimization community detection method based on affinity propagation. According to the evolutionary multi-objective optimization community detection method based on affinity propagation, a complex network can be partitioned more precisely and rapidly.

Description

technical field [0001] The invention belongs to the field of computers, and relates to an evolutionary multi-objective optimization community detection method based on neighbor propagation, which can be used for mining community structures in artificial networks and real-world networks. Background technique [0002] In real life, complex networks are used to represent the interrelationships of individuals within different systems or between systems. Such as the Internet, biological networks of natural populations, and interpersonal social networks. Community detection in complex networks refers to finding sub-networks composed of nodes with the same characteristics in complex networks. Therefore, to better analyze the structure and function of complex networks, community detection has become an important research direction in recent years. [0003] At present, the methods of community detection mainly include hierarchical clustering algorithm based on node similarity, grap...

AI Technical Summary

Problems solved by technology

[0004] However, due to the current evolutionary algorithm used in the community detection problem, the initial population has strong randomness, low quality, and poor local search ability, resulting in slow convergence of the algorithm.

At the same time, the evolutionary algorithm is prone to premature and degenerate problems, so the final result is less accurate

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