PME: Projected Metric Embedding On Heterogeneous Networks For Link Prediction

Authors:
Hongxu Chen The University of Queensland
Hongzhi Yin The University of Queensland
Weiqing Wang The University of Queensland
Hao Wang Qihoo 360 Inc
Quoc Viet Hung Nguyen Griffith University
Xue Li The University of Queensland

Introduction:

This paper studies Heterogenous information network embedding. To alleviate the potential geometrical inflexibility of existing metric learning approaches, the authors propose to build object and relation embeddings in separate object space and relation spaces rather than in a common space.

Abstract:

Heterogenous information network embedding aims to embed heterogenous information networks (HINs) into low dimensional spaces, in which each vertex is represented as a low-dimensional vector, and both global and local network structures in the original space are preserved. However, most of existing heterogenous information network embedding models adopt the dot product to measure the proximity in the low dimensional space, and thus they can only preserve the first-order proximity and are insufficient to capture the global structure. Compared with homogenous information networks, there are multiple types of links (i.e., multiple relations) in HINs, and the link distribution w.r.t relations is highly skewed. To address the above challenging issues, we propose a novel heterogenous information network embedding model PME based on the metric learning to capture both first-order and second-order proximities in a unified way. To alleviate the potential geometrical inflexibility of existing metric learning approaches, we propose to build object and relation embeddings in separate object space and relation spaces rather than in a common space. Afterwards, we learn embeddings by firstly projecting vertices from object space to corresponding relation space and then calculate the proximity between projected vertices. To overcome the heavy skewness of the link distribution w.r.t relations and avoid “over-sampling’’ or “under-sampling’’ for each relation, we propose a novel loss-aware adaptive sampling approach for the model optimization. Extensive experiments have been conducted on a large-scale HIN dataset, and the experimental results show superiority of our proposed PME model in terms of prediction accuracy and scalability.

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