计算机工程2017，Vol.43Issue(11)：182-186,5.DOI:10.3969/j.issn.1000-3428.2017.11.029
基于随机游走理论的改进LFM算法
Improved LFM Algorithm Based on Random Walk Theory
摘要
Abstract
Traditional LFM community discovery algorithm based on local information of network to realize community detection.However,the structure information in the whole network is underused.It leads to serious decline of algorithm precision in network with fuzzing community structure.What's more,because of community expansion by local information,it's easy for LFM to get abnormal community division.In order to solve the above problem,an improved LFM algorithm is proposed.Random walk theory is used to measure the similarity of nodes,so that the community structure is clearer.Meanwhile,in order to avoid abnormal community division,maximal clique in the weighted network is found and used to expand the community.The experimental results on artificial networks and real networks show that the improved LFM algorithm achieves higher classification accuracy than traditional LFM algorithm and Label Propagation Algorithm (LPA).关键词
复杂网络/社团发现/LFM算法/随机游走/极大子团Key words
complex network/community detection/LFM algorithm/random walk/maximal clique分类
信息技术与安全科学引用本文复制引用
杨晓波,陈楚湘,王至婉..基于随机游走理论的改进LFM算法[J].计算机工程,2017,43(11):182-186,5.基金项目
国家自然科学基金(81574100). （81574100）
