He Dongxiao, Jin Di, Chen Zheng, Zhang Weixiong
School of Computer Science and Technology, Tianjin University, Tianjin. 300072, P. R. China.
1] Department of Computer Science and Engineering, Washington University, St. Louis. MO 63130, USA [2] Institute for Systems Biology, Jianghan University, Wuhan. Hubei 430056, P. R. China.
Sci Rep. 2015 Mar 2;5:8638. doi: 10.1038/srep08638.
Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.
识别复杂网络中的社区是分析复杂系统的有效手段，在社会科学、工程学、生物学和医学等不同领域都有应用。寻找节点社区和寻找链接社区是网络分析的两种常见方案。然而，这些方案存在固有缺陷，不足以捕捉真实网络中的复杂组织结构。我们引入了一种新的方案和有效方法来识别节点和链接社区的复杂混合结构，即混合节点-链接社区。我们方法的核心是一个概率模型，该模型适用于节点、链接和混合节点-链接社区。我们在各种真实网络上进行的广泛实验，包括一个大型蛋白质-蛋白质相互作用网络和一个大型语义相关词网络，表明混合社区方案在揭示网络特征方面更具优势。此外，新方法在分别寻找节点或链接社区方面优于现有方法。
