Meta-Path based Nonnegative Matrix Factorization for clustering on multi-type relational data
Clustering on multi-type relational data has attracted increasing interest due to its great practical and theoretical importance. One of the most popular solutions is nonnegative matrix factorization. However, previous work on nonnegative matrix factorization typically copes with multi-type relations individually, and ignores the underlying semantics conveyed by the relation propagation. Additionally, these approaches may suffer from data sparsity as most of the relations between object pairs are unknown. In this paper we propose a novel Meta-Path based Nonnegative Matrix Factorization (MPNMF) framework, which enriches potentially useful similarity semantics for the improved clustering performance. We begin with constructing meta-paths, i.e., paths that connects object types via a sequence of relations, which are appropriately weighted according to certain propagation decay rules. Based on the weighted meta-paths, we are promised to characterize the strength of pairwise interactions among the objects. Together with the attributes in the bag-of-word form, we cluster the objects of target type by collective nonnegative matrix factorization. Experiments on real world datasets demonstrate the effectiveness of our method.