A Semantic Aware Meta-Path Model for Heterogeneous Network Representation Learning

Abstract

Heterogeneous graph representation learning is to learn effective representations for nodes or (sub)graphs, which preserve node attributes and structural information. However, it is challenging to design a representation learning method for heterogeneous information networks (HINs) due to their diversity. Most of the existing HIN-oriented learning methods define a series of meta-paths. Then, they aggregate the representations learned from different meta-paths in the same hidden space. These methods do not consider semantic differences of different meta-paths, which leads to semantic confusion. And further affects the effectiveness of the learned representation. Given these issues, we introduce a Semantic Aware HIN Representation learning Network (SAHRN), which takes into account the semantics of different meta-paths. We mitigate the problem of semantic confusion by projecting nodes’ features into different hidden spaces separately according to different meta-paths. To further expand the scope of aggregation and enrich the aggregated information, we also design various variants of our model by adding layer aggregation. Extensive experiments on three standard HIN datasets show that SAHRN achieves consistent improvements compared to state-of-the-art graph representation learning methods. The experiments and analyses on each component of the model show the effectiveness of the proposed method. The source code is available on https://github.com/pingpingand/SAHRN .