Multi-objective community detection method based on spectrum information of common neighbour matrix

Abstract

The invention provides a multi-objective community detection method based on spectrum information of a modified common neighbours matrix, which mainly solves the problems of existing community detection methods that the resolution is low and time complexity of large scale networks is high. The detection method is achieved by the steps of constructing a common neighbours matrix and modifying the matrix according to a network; extracting spectrum information used for modifying the common neighbours matrix; initializing a parent memory bank by using the spectrum information, and calculating the fitness of the parent memory bank; generating a descendant memory bank from the parent memory bank through a harmony search arithmetic, and calculating the fitness of the descendant memory bank; combining the parent memory band and an descendant memory bank, and performing non-dominated sorting to obtain a temporary memory bank; performing partial learning about the temporary memory bank to obtain an updated temporary memory bank, and acquiring a parent memory bank in the next iteration from the updated temporary memory bank; and if maximum iterations are reached, extracting all non-dominated solutions from the parent memory bank as a final solution set, otherwise iterations continue. The multi-objective community detection method based on spectrum information of the modified common neighbours matrix has the advantages of being high in resolution of community detections and capable of reducing time complexity.

Description

technical field [0001] The invention belongs to the technical field of complex networks and multi-objective optimization, and relates to data mining technology in complex networks, spectrum information of co-adjacent matrix and multi-objective optimization technology in evolutionary calculation, which is used for community detection in complex networks and can discover networks at the same time multi-scale community structure. Background technique [0002] The rapid development of information technology represented by the Internet has brought human society into the Internet age. Many systems in the real world can be described in the form of complex networks, such as interpersonal networks in social systems, scientists' cooperation networks and epidemic transmission networks, neuron networks in ecosystems, email networks in technological systems, Internet and World Wide Web, large power networks in power systems, etc. Complex network theory mainly studies the commonalities ...

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Problems solved by technology

[0011] In addition, in the optimization method, many objectives have been proposed successively, such as modularity density, community scores, community fitness, etc. proposed to solve the limitation of modularity resolution, but these methods are basically single-objective methods, each time only A kind of community structure of the network can be found, and these methods are basically based on the encoding method of genetic neighbors or community numbers, the encoding is long, and there is a problem of high time complexity for large and complex networks. At the same time, many problems have been proposed. Objective optimization methods, such as MOGA-Net proposed by C.Pizzuti in "A Multi-objective Genetic Algorithm for Community Detection in Networks" (Proceedings of the 21st IEEE International Conference on Tools with Artificial Intelligence, pp.379-386, 2009) Algorithms, but these methods have low accuracy and the effect is not ideal

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