Efficient heterogeneous proximity preserving network embedding model

Abstract

We study the problem of representation learning in heterogeneous information networks. Its unique challenges come from the existence of multiple types of vertices and edges. Existing proximity-based network embedding techniques ignore the type information when evaluating the proximity and limits their usage in heterogeneous scenario. In this paper, we propose a heterogeneous proximity preserving network embedding model via meta path guided random walk, which is capable of capturing the high-order proximity between vertices specified by the given path. To improve the learning efficiency, we introduce a sampling based learning strategy which can incrementally learn representations. We conduct experiments on two real world heterogeneous information networks. Experimental results on several mining tasks prove the effectiveness of our approach over many competitive baselines. The model is very efficient and is able to learn embeddings for large networks both in offline and online scenarios. Besides, for expert system, our approach can be applied to improve the representation of knowledge entities by depicting the knowledge base as a heterogeneous information network.