摘要： 传统局部扩张方法在对企业社会化网络（ESN）中的重叠社区结构进行识别时，存在计算冗余与社区挖掘不彻底的问题。为此，提出一种基于贪婪派系扩张的重叠社区发现算法GFE。在原始ESN中寻找极大派系，根据派系间的关联程度计算其链接强度，将原始网络图转换成最大派系图。在最大化适应度函数的条件下，贪婪扩张最大派系图中的种子派系，以进行社区发现。在此基础上，比较社区差异度，合并近似重复的社区，从而优化重叠社区的层次结构。实验结果表明，GFE算法能有效发现ESN中的重叠社区结构，且运行效率高于CPM、LFM等算法。

关键词: 贪婪派系扩张, 极大派系, 企业社会化网络, 社区发现, 重叠社区

Abstract: There are problems of computational redundancy and incomplete community mining in traditional local expansion methods for identifying overlapping community structures in Enterprise Social Network(ESN).Therefore,an overlapping community discovery algorithm GFE based on greedy factional expansion is proposed.GFE algorithm searches for maximal factions in the original ESN,calculates their link strength according to the degree of association between factions,and converts the original network graph into the maximal faction graph.Under the condition of maximizing fitness function,the seed factions in the maximal faction graph are greedily expanded for community discovery.On this basis,the community differences are compared,and the similar duplicated communities are merged to optimize the hierarchical structure of overlapping community.Experimental results show that the GFE algorithm can effectively discover overlapping community structure in ESN,and the operation efficiency is higher than those of CPM, LFM and other algorithms.

Key words: greedy factional expansion, maximal faction, Enterprise Social Network(ESN), community discovery, overlapping community

中图分类号: 

• TP391