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Many real-world graphs involve different types of nodes and relations betweennodes, being heterogeneous by nature. The representation learning ofheterogeneous graphs (HGs) embeds the rich structure and semantics of suchgraphs into a low-dimensional space and facilitates various data mining tasks,such as node classification, node clustering, and link prediction. In thispaper, we propose a self-supervised method that learns HG representations byrelying on knowledge exchange and discovery among different HG structuralsemantics (meta-paths). Specifically, by maximizing the mutual information ofmeta-path representations, we promote meta-path information fusion andconsensus, and ensure that globally shared semantics are encoded. By extensiveexperiments on node classification, node clustering, and link prediction tasks,we show that the proposed self-supervision both outperforms and improvescompeting methods by 1% and up to 10% for all tasks.

HeMI: Multi-view Embedding in Heterogeneous Graphs