LBLP：Link-Clustering-Based Approach for Overlapping Community Detection

• Published in: Tsinghua science and technology 2013-08, Vol.18 (4), p.387-397
• Main Authors: Yu, Le, Wu, Bin, Wang, Bai
• Format: Article
• Language: English
• Subjects: Allocations; 复杂网络; 市场营销; 环境领域; 社区; 群落结构; 聚类; 路基; 链接
• ISSN: 1007-0214; 1878-7606; 1007-0214
• DOI: 10.1109/TST.2013.6574677

Recently, complex networks have attracted considerable research attention. Community detection is an important problem in the field of complex networks and is useful in a variety of applications such as information propagation, link prediction, recommendation, and marketing. In this study, we focus on discovering overlapping community structures by using link partitions. We propose a Latent Dirichlet Allocation （LDA）-Based Link Partition （LBLP） method, which can find communities with an adjustable range of overlapping. This method employs the LDA model to detect link partitions, which can calculate the community belonging factor for each link. On the basis of this factor, link partitions with bridge links can be found efficiently. We validate the effectiveness of the proposed solution by using both real-world and synthesized networks. The experimental results demonstrate that the approach can find a meaningful and relevant link community structure.