Open AccessUncovering Community Structures with Initialized Bayesian Nonnegative Matrix Factorization
Author(s) -
Xianchao Tang,
Tao Xu,
Feng Xia,
Guangsong Yang
Publication year - 2014
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0107884
Subject(s) - matrix decomposition , non negative matrix factorization , singular value decomposition , computer science , matrix (chemical analysis) , factorization , algorithm , nonnegative matrix , adjacency matrix , community structure , sparse matrix , mathematics , symmetric matrix , theoretical computer science , combinatorics , eigenvalues and eigenvectors , graph , physics , materials science , quantum mechanics , composite material , gaussian
Uncovering community structures is important for understanding networks. Currently, several nonnegative matrix factorization algorithms have been proposed for discovering community structure in complex networks. However, these algorithms exhibit some drawbacks, such as unstable results and inefficient running times. In view of the problems, a novel approach that utilizes an initialized Bayesian nonnegative matrix factorization model for determining community membership is proposed. First, based on singular value decomposition, we obtain simple initialized matrix factorizations from approximate decompositions of the complex network’s adjacency matrix. Then, within a few iterations, the final matrix factorizations are achieved by the Bayesian nonnegative matrix factorization method with the initialized matrix factorizations. Thus, the network’s community structure can be determined by judging the classification of nodes with a final matrix factor. Experimental results show that the proposed method is highly accurate and offers competitive performance to that of the state-of-the-art methods even though it is not designed for the purpose of modularity maximization.