Complex network community detection method capable of improving global convergence performance

Abstract

The invention discloses a complex network community detection method capable of improving global convergence performance. The method specifically includes the step of improving the global convergenceperformance of a differential evolution algorithm, and the step includes the specific steps of 1, classification of adaptive differential classification mutation strategies; 2, dynamic adaptive parameter adjustment; 3, differential selection operation based on historical information. The classification-based adaptive mutation acts on all individuals in each generation of population till the end ofevolution, so that mutation of each individual can be subjected to targeted adjustment. On the one hand, the exploration capability of excellent individuals can be improved to increase the possibility of finding global optimization in neighborhood regions; on the other hand, the mining capability of poor individuals can be improved to accelerate the search speed to the global optimization.

Description

technical field [0001] The invention relates to a community detection method, in particular to a complex network community detection method capable of improving global convergence performance. Background technique [0002] In the past few years, many community detection methods have been proposed, and the most widely used method is the optimization method based on modularity. However, modularity optimization is essentially a typical NP-hard problem. Traditional deterministic optimization algorithms, such as mathematical programming, greedy algorithms, spectral analysis methods, and extreme value optimization algorithms, usually have premature convergence or convergence stagnation. . In addition, as the real-world network scale and structural ambiguity increase, the extremum degradation problem in the optimization process becomes more serious, which means that among the exponentially growing number of local optimal solutions, finding the global optimal community The divisio...

AI Technical Summary

Problems solved by technology

However, as far as we know, in existing algorithms, basic EAs are usually directly used as optimization strategies and their convergence ability is ignored, which leads to premature convergence of EAs and poor quality of the optimal community division obtained.

At the same time, although some existing algorithms have improved the evolutionary operation in EAs to meet the needs of community detection by fusing network topology information, the inappropriate use of topology information destroys the search space for global optimal community division.

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