| Summary: | Existing network embedding models are mostly designed for homogeneous networks or heterogeneous networks, but ignore the special features of bipartite network which arise in recommender systems, search engines, question answering systems and so on. Meanwhile bipartite networks mostly include rich attribute information. To address the above challenging issues, this paper proposes ABNE (attributed bipartite network embedding). Specifically, ABNE first preserves the explicit relations in the bipartite network by decomposing edges into sets of indirect relationships between neighborhood nodes. Then it calculates attribute similarity matrix by cosine similarity and as part of the weight matrix to guide the biased random walk to embed implicit relations and attribute information. Finally, ABNE introduces an optimization framework to obtain the node representation vector which carries both structure information and attribute information. Several tasks have been conducted on four public datasets and compared with other state-of-the-art embedding models. The experimental results show superiority and rationality of ABNE model.
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