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Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks. (arXiv:2112.14936v1 [cs.LG])
cs.LG updates on arXiv.org arxiv.org
Heterogeneous graph neural networks (HGNNs) have been blossoming in recent
years, but the unique data processing and evaluation setups used by each work
obstruct a full understanding of their advancements. In this work, we present a
systematical reproduction of 12 recent HGNNs by using their official codes,
datasets, settings, and hyperparameters, revealing surprising findings about
the progress of HGNNs. We find that the simple homogeneous GNNs, e.g., GCN and
GAT, are largely underestimated due to improper settings. GAT with proper …
arxiv benchmarking graph graph neural networks making networks neural networks progress