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

近年来,随着社交网络的发展,许多重叠社区挖掘算法被提出来.传统的方法都是将节点作为研究对象,而最近的一些研究表明,以边为研究对象的边社区挖掘方法相对于点社区挖掘方法来说具有更加明显的优势.因此,我们提出了基于局部边社区的挖掘算法(LLCM),利用网络中的局部信息去挖掘边社区结构.给定一条初始的边,通过不断最大化一个适应度函数来获取该边所在的局部社区,而这条初始的边可以预先通过一些排序算法进行选择.算法经过在计算机生成网络和真实网络上测试,并且同其他边社区挖掘算法进行了比较,实验结果表明LLCM算法获取了合理的边社区的结构.

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.