Nonnegative Matrix Factorization Algorithm with Prior Information for Community Detection
LI Guopeng1,2, PAN Zhisong1, YAO Qing3, LI Deyi1,4
1.College of Command Information System, PLA University of Science and Technology, Nanjing 210007 2.Xi′an Communications Institute, Xi′an 710106 3.China Electronic Equipment System Engineering Company, Beijing 100079 4.Institute of China Electronic System Engineering Corporation, Beijing 100039
Abstract To solve the problem of community detection in complex networks, a semi-supervised nonnegative matrix factorization (NMF) algorithm with prior information is proposed to obtain more accurate and better understanding results, and the detailed iteration algorithm is presented. In this algorithm, prior information is added to object function as additional constraints in community indicator matrix. Consequently, results are more meaningful. The experiments on real-world network datasets confirm the effectiveness of the proposed algorithm. It reduces the negative impact of the addition of prior information on node importance analysis with NMF, and it is suitable for weighted and un-weighted networks.
About author:: LI Guopeng, born in 1982, Ph.D. candidate. His research interests include computational intelligence and pattern recognition. PAN Zhisong(Corresponding author), born in 1973, Ph.D.,professor. His research interests include pattern recognition and machine learning.YAO Qing, born in 1983, Ph.D., engineer. Her research interests include cloud computing.LI Deyi, born in 1944, Ph.D., professor. His research interests include artificial intelligence.
LI Guopeng,PAN Zhisong,YAO Qing等. Nonnegative Matrix Factorization Algorithm with Prior Information for Community Detection[J]. , 2016, 29(7): 608-615.
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2016, Vol. 29 
