Title
Community Detection Based on Co-regularized Nonnegative Matrix Tri-Factorization in Multi-view Social Networks
Abstract
Social network is a hot issue in recent years. In this field, data with multiple views or data from multiple sources are referred to as multi-view data. Due to the uncertain quality of data source, single-view method often leads to unstable performance in community discovery. However, to combine various numbers of views to improve community detection performance is a challenge. In this paper, we propose a method called CoNMTF (Co-regularized Nonnegative Matrix Tri-Factorization). A relaxed pairwise regularization is introduced to integrate multiview adjacency data. Under this framework, we propose an iterative algorithm and prove its correctness and convergence. Experimental results on both synthetic datasets and real-world datasets demonstrate that it outperforms the-state-of-art algorithms in terms of accuracy and NMI.
Year
DOI
Venue
2018
10.1109/BigComp.2018.00023
2018 IEEE International Conference on Big Data and Smart Computing (BigComp)
Keywords
Field
DocType
multi-view social networks,community detection,nonnegative matrix tri-factorization (NMTF),big data
Adjacency list,Pairwise comparison,Nonnegative matrix,Computer science,Iterative method,Correctness,Algorithm,Symmetric matrix,Factorization,Cluster analysis
Conference
ISSN
ISBN
Citations 
2375-933X
978-1-5386-3650-3
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Longqi Yang102.70
Liangliang Zhang201.35
ZhiSong Pan37320.41
GuYu Hu43415.21
Yanyan Zhang514923.51