Variational Graph Auto-Encoders for Heterogeneous Information Network

Abstract

Heterogeneous Information Network (HIN), where nodes and their attributes denote real-world entities and links encode relationships between entities, are ubiquitous in many applications. The presence of multiple types of nodes and links pose significant challenges to the state-of-the-art methods for learning node embeddings from heterogeneous graphs. To address these challenges, we consider three variants of graph variational autoencoder models for heterogeneous networks that avoid the computationally expensive sampling of meta-paths. The proposed methods also maintain uncertainty estimates of node embeddings that help improve generalization performance. We report the results of experiments on link prediction using three different real-world heterogeneous network benchmark data sets that show that the proposed methods significantly outperform state-of-the-art baselines.