Identifying Community in Bipartite Networks Using Graph Regularized-based Non-negative Matrix Factorization

Wang Tao; Liu Yang; Xi Yao-yi

doi:10.11999/JEIT141649

Volume 37 Issue 9

Sep. 2015

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Wang Tao, Liu Yang, Xi Yao-yi. Identifying Community in Bipartite Networks Using Graph Regularized-based Non-negative Matrix Factorization[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2238-2245. doi: 10.11999/JEIT141649

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Wang Tao, Liu Yang, Xi Yao-yi. Identifying Community in Bipartite Networks Using Graph Regularized-based Non-negative Matrix Factorization[J]. Journal of Electronics & Information Technology, 2015, 37(9): 2238-2245. doi: 10.11999/JEIT141649

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Identifying Community in Bipartite Networks Using Graph Regularized-based Non-negative Matrix Factorization

doi: 10.11999/JEIT141649

Received Date: 2014-12-26
Rev Recd Date: 2015-04-13
Publish Date: 2015-09-19

Abstract

Abstract

There are many bipartite networks composed of two types of nodes in the real world, studying the community structure of them is helpful to understand the complex network from a new point of view. Non- negative matrix factorization can overcome the limitation of the two-mode structure of bipartite networks, but it is also subject to several problems such as slow convergence and large computation. In this paper, a novel algorithm using graph regularized-based non-negative matrix factorization is presented for community detection in bipartite networks. It respectively introduces the internal connecting information of two-kinds of nodes into the Non- negative Matrix Tri-Factorization (NMTF) model as the graph regularizations. Moreover, this paper divides NMTF into two sub problems of minimizing the approximation error, and presents an alternative iterative algorithm to update the factor matrices, thus the iterations of matrix factorization can be simplified and accelerated. Through the experiments on both computer-generated and real-world networks, the results and analysis show that the proposed method has superior performances than the typical community algorithms in terms of the accuracy and stability, and can effectively discover the meaningful community structures in bipartite networks.
- Bipartite networks,
- Community detection,
- Graph regularized,
- Non-negative matrix factorization

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References

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