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Recent studies have highlighted the limitations of message-passing basedgraph neural networks (GNNs), e.g., limited model expressiveness,over-smoothing, over-squashing, etc. To alleviate these issues, GraphTransformers (GTs) have been proposed which work in the paradigm that allowsmessage passing to a larger coverage even across the whole graph. Hinging onthe global range attention mechanism, GTs have shown a superpower forrepresentation learning on homogeneous graphs. However, the investigation ofGTs on heterogeneous information networks (HINs) is still under-exploited. Inparticular, on account of the existence of heterogeneity, HINs show distinctdata characteristics and thus require different treatment. To bridge this gap,in this paper we investigate the representation learning on HINs with GraphTransformer, and propose a novel model named HINormer, which capitalizes on alarger-range aggregation mechanism for node representation learning. Inparticular, assisted by two major modules, i.e., a local structure encoder anda heterogeneous relation encoder, HINormer can capture both the structural andheterogeneous information of nodes on HINs for comprehensive noderepresentations. We conduct extensive experiments on four HIN benchmarkdatasets, which demonstrate that our proposed model can outperform thestate-of-the-art.

HINormer: Representation Learning On Heterogeneous Information Networks with Graph Transformer