caa a22 4500 445871385 CHVBK 20180317145512.0 cr unu---uuuuu 170323e20110501xx s 000 0 eng 10.1007/s10618-010-0181-y doi (NATIONALLICENCE)springer-10.1007/s10618-010-0181-y Community discovery using nonnegative matrix factorization [Elektronische Daten] [Fei Wang, Tao Li, Xin Wang, Shenghuo Zhu, Chris Ding] 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. The Author(s), 2010 Community discovery nationallicence Nonnegative matrix factorization nationallicence Wang Fei School of Computing and Information Sciences, Florida International University, Miami, FL, USA aut Li Tao School of Computing and Information Sciences, Florida International University, Miami, FL, USA aut Wang Xin School of Computing and Information Sciences, Florida International University, Miami, FL, USA aut Zhu Shenghuo NEC Research Lab America at Cupertino, Cupertino, CA, USA aut Ding Chris Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA aut Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 22/3(2011-05-01), 493-521 1384-5810 22:3<493 2011 22 10618 https://doi.org/10.1007/s10618-010-0181-y text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s10618-010-0181-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Wang Fei School of Computing and Information Sciences, Florida International University, Miami, FL, USA aut NATIONALLICENCE 700 1- Li Tao School of Computing and Information Sciences, Florida International University, Miami, FL, USA aut NATIONALLICENCE 700 1- Wang Xin School of Computing and Information Sciences, Florida International University, Miami, FL, USA aut NATIONALLICENCE 700 1- Zhu Shenghuo NEC Research Lab America at Cupertino, Cupertino, CA, USA aut NATIONALLICENCE 700 1- Ding Chris Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA aut NATIONALLICENCE 773 0- Data Mining and Knowledge Discovery Springer US; http://www.springer-ny.com 22/3(2011-05-01), 493-521 1384-5810 22:3<493 2011 22 10618 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer