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Heterogeneous Sparse Relational Data Co-clustering in Social Network

In social network, users generated multi-typed entities and complex interactive relations. The relational data mining is a hot research in social computing. Co-clustering algorithms have been proposed to mine underlying structure of different entities in heterogeneous social network. However, the real heterogeneous relational data are very sparse. In this paper, we propose a fast High-order Sparse Non-negative Matrix Factorization algorithm to co-cluster heterogeneous sparse relational data based on Correlation Matrix(HSNMF-CM), which is built by the correlation relations of small entities. In HSNMF-CM, the sparseness and size of matrix are reduced simultaneously. Under the sparse constraint, the block coordinate descent algorithms are used to accelerate the convergence rate of the matrix factorization. We assess the performance of the HSNMF-CM on two social data sets. The results show that our algorithm outperforms state-of-the-art algorithms on accuracy and convergence speed, and possesses a high scalability on large-scale heterogeneous relational data sets.