Community discovery using nonnegative matrix factorization
Community discovery using nonnegative matrix factorization
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DOI：
10.1007/s10618-010-0181-y
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发表时间：
2011-05-01
影响因子：
4.8
通讯作者：
Ding, Chris
中科院分区：
文献类型：
--
作者：
Wang, Fei;Li, Tao;Ding, Chris
复杂网络存在于广泛的现实世界系统中，如社会网络、技术网络和生物网络。在过去的几十年里，许多研究人员都集中在探索这些大网络中包含的一些共同的东西，包括小世界性质、幂律度分布和网络连通性。在本文中，我们将探讨网络分析中的另一个重要问题——社区发现。我们选择非负矩阵分解（NMF）作为我们的工具，因为它具有强大的可解释性和紧密的聚类方法之间的关系。针对不同类型的网络（无向、有向和复合），我们提出了三种NMF技术（对称NMF、不对称NMF和联合NMF）。研究了这些算法的正确性和收敛性。最后，在实际网络上进行了实验，验证了所提方法的有效性。
Complex networks exist in a wide range of real world systems, such as social networks, technological networks, and biological networks. During the last decades, many researchers have concentrated on exploring some common things contained in those large networks include the small-world property, power-law degree distributions, and network connectivity. In this paper, we will investigate another important issue, community discovery, in network analysis. We choose Nonnegative Matrix Factorization (NMF) as our tool to find the communities because of its powerful interpretability and close relationship between clustering methods. Targeting different types of networks (undirected, directed and compound), we propose three NMF techniques (Symmetric NMF, Asymmetric NMF and Joint NMF). The correctness and convergence properties of those algorithms are also studied. Finally the experiments on real world networks are presented to show the effectiveness of the proposed methods.