计算机应用与软件2017，Vol.34Issue(7)：217-221,297,6.DOI:10.3969/j.issn.1000-386x.2017.07.040
基于资源分配指标的最大约束社区发现算法
MAXIMUM CONSTRAINED COMMUNITY DETECTION ALGORITHM BASED ON RESOURCE ALLOCATION INDEX
摘要
Abstract
Community detection in complex networks have received wide attention, and the method based on modularity maximization is the popular community detection technology.In this paper, a modularity maximization community detection algorithm named RALPA (Resource Allocation-based of Label Propagation Algorithm) is proposed, which is based on resource allocation (RA) and multi-step greedy cohesion strategy.The algorithm uses the RA index to measure the similarity between nodes accurately.By using the maximum constraint label propagation model, the internal nodes of the community have high similarity, and have low similarity with the nodes outside the community.Then, through the multi-step greedy cohesion strategy, the multi-pair small communities with the largest increase of partitioning degree will be merged.The experimental results show that the proposed algorithm not only avoids the problem of the sensitivity of node update order and the trivial solution, but also improves the stability of the algorithm and the accuracy of community division.关键词
社区发现/模块度最大化/资源分配指标/最大约束标记传播模型Key words
Community detection/ Modularity optimization/ Resource allocation/ Maximum constraint label propagation model分类
信息技术与安全科学引用本文复制引用
宁念文,许合利,刘喜峰..基于资源分配指标的最大约束社区发现算法[J].计算机应用与软件,2017,34(7):217-221,297,6.基金项目
国家自然科学基金项目(61202286) （61202286）
国家科技重大专项核心电子器件、高端通用芯片及基础软件产品专项(2014ZX01045-102). （2014ZX01045-102）
