Heterogeneous Graph Embedding with Single-Level Aggregation and Infomax Encoding

Abstract

There has been an increasing interest in developing embedding methods for heterogeneous graph-structured data. The state-of-the-art approaches often adopt a bi-level aggregation scheme, where the first level aggregates information of neighbors belonging to the same type or group, and the second level employs the averaging or attention mechanism to aggregate the outputs of the first level. We find that bi-level aggregation may suffer from a down-weighting issue and overlook individual node information, especially when there is an imbalance in the number of different typed relations. We develop a new simple yet effective single-level aggregation scheme with infomax encoding, named HIME, for unsupervised heterogeneous graph embedding. Our single-level aggregation scheme performs relation-specific transformation to obtain homogeneous embeddings before aggregating information from multiple typed neighbors. Thus, it emphasizes each neighbor’s equal contribution and does not suffer from the down-weighting issue. Extensive experiments demonstrate that HIME consistently outperforms the state-of-the-art approaches in link prediction, node classification, and node clustering tasks.