电子学报2012，Vol.40Issue(11)：2255-2263,9.DOI:10.3969/j.issn.0372-2112.2012.11.018
社会网络中基于局部信息的边社区挖掘
Detecting Link Communities Based on Local Information in Social Networks
摘要
Abstract
Recent years have seen the development of online social networks.Many algorithms have been proposed that are able to assign each node to more than a single community. The traditional approaches were always focusing on the node community, while some recent studies have shown great advantage of link community approach which partitions links instead of nodes into communities. In this paper, we present a novel algorithm LLCM (local link community mining algorithm) for discovering link communities in networks. A local link community can be detected by maximizing a local link fitness function from a seed link, which was ranked previously.The proposed LLCM algorithm has been tested on both synthetic and real world networks,and it has been compared with other link community detecting algorithms. The experimental results showed LLCM achieves significant improvement on link community structure.关键词
社区挖掘/边社区/局部社区Key words
community detection /link community/ local community分类
信息技术与安全科学引用本文复制引用
潘磊,金杰,王崇骏,谢俊元..社会网络中基于局部信息的边社区挖掘[J].电子学报,2012,40(11):2255-2263,9.基金项目
国家自然基金(No.60503021,No.60721002,No.60875038,No.61105069) （No.60503021,No.60721002,No.60875038,No.61105069）
江苏省科技支撑计划(No.RE2010180,No.BE2011171) （No.RE2010180,No.BE2011171）
南京大学研究生创新基金(No.2011CL07) （No.2011CL07）
