Metapath Enhanced Graph Attention Encoder for HINs Representation Learning

Abstract

In this paper, we propose a novel representation learning framework, named MEGAE, for heterogeneous information networks. To investigate the rich semantic information in heterogeneous information networks, we use metapaths to complete implicit links between nodes. A graph attention encoder is further used to learn graph structural information with shared weight parameters. The attention mechanism, on the other hand, provides us an intuition of how the representation is learned and improves the interpretability of our model. Furthermore, a multitask learning of node classification and link prediction is trained to achieve more robust generalization ability. To validate our ideas, extensive experiments on three real-world datasets show that our model achieves state-of-the-art results on node classification and link prediction tasks in HINs.