广西师范大学学报（自然科学版）2024，Vol.42Issue(2)：105-119,15.DOI:10.16088/j.issn.1001-6600.2023052903
基于矩阵运算加速的改进社区发现遗传算法
Genetic Algorithm for Community Detection Accelerated by Matrix Operations
摘要
Abstract
Community detection algorithms are critical research tools in the field of complex networks.However,traditional community detection genetic algorithms have the problems with poor initial population quality and low computational efficiency under large-scale networks.To address this,an improved community detection genetic algorithm based on matrix computation acceleration is proposed.To tackle the problem of subpar initial population quality,a novel initialization operator is proposed to construct high-quality initial communities using the closure coefficient with biases node selection.To address the issue of low computational efficiency,the traditional community detection genetic algorithm operators are restructured based on matrix operations,enabling the use of GPU acceleration to enhance computational efficiency.Experimental results indicate that the proposed algorithm maintains good partitioning accuracy and demonstrates higher computational efficiency than other algorithms of the same type under different scales of real networks and LFR synthetic networks.关键词
复杂网络/社区发现/遗传算法/矩阵运算/模块度Key words
complex network/community detection/genetic algorithm/matrix operation/modularity分类
信息技术与安全科学引用本文复制引用
余谦,陈庆锋,何乃旭,韩宗钊,卢家辉..基于矩阵运算加速的改进社区发现遗传算法[J].广西师范大学学报（自然科学版）,2024,42(2):105-119,15.基金项目
国家自然科学基金(61963004,61862006) （61963004,61862006）
