Method for detecting complex network communities

Abstract

The invention discloses a method for detecting complex network communities. In order to improve the global convergence performance of a differential evolution algorithm, the method comprises the stepof redesigning three main evolutionary operations: a classification-based self-adaptive mutation strategy, a dynamic self-adaptive parameter adjusting strategy and a history information-based selection operation; on the other hand, in order to make better use of network topology information, proposing an improved neighborhood information-based community adjusting strategy to ensure that sufficientsearch space is provided for global optimal community division while the DE (Differential Evolution) search space is reduced at the same time; and finally, proposing a new modularity optimizing algorithm CDEMO (C Differential Evolution for Multiobjective Optimization) based on the DE algorithm.

Description

technical field [0001] The invention relates to a community detection method, in particular to a complex network community detection method. 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 division becomes more difficult, thus seriously affecting t...

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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