Global and Nodal Mutual Information Maximization in Heterogeneous Graphs

Many real-world graphs involve different types of nodes and edges, being heterogeneous by nature. Heterogeneous graph representation learning embeds their rich structure and semantics into a low-dimensional space to facilitate graph related tasks. In this work, we propose a self-supervised method that learns representations by relying on mutual information maximization among different graph structures (metapaths). Our method, termed HeMI, promotes node-level and global-level shared semantics among nodes with contrastive learning, as well as it leverages interactions among metapaths. Experiments on node classification, node clustering, and link prediction show that HeMI outperforms existing approaches.