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Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
Jiong Zhu · Yujun Yan · Lingxiao Zhao · Mark Heimann · Leman Akoglu · Danai Koutra

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #374

We investigate the representation power of graph neural networks in the semi-supervised node classification task under heterophily or low homophily, i.e., in networks where connected nodes may have different class labels and dissimilar features. Many popular GNNs fail to generalize to this setting, and are even outperformed by models that ignore the graph structure (e.g., multilayer perceptrons). Motivated by this limitation, we identify a set of key designs—ego- and neighbor-embedding separation, higher-order neighborhoods, and combination of intermediate representations—that boost learning from the graph structure under heterophily. We combine them into a graph neural network, H2GCN, which we use as the base method to empirically evaluate the effectiveness of the identified designs. Going beyond the traditional benchmarks with strong homophily, our empirical analysis shows that the identified designs increase the accuracy of GNNs by up to 40% and 27% over models without them on synthetic and real networks with heterophily, respectively, and yield competitive performance under homophily.

Author Information

Jiong Zhu (University of Michigan)
Yujun Yan (University of Michigan)
Lingxiao Zhao (Carnegie Mellon University)
Mark Heimann (Lawrence Livermore National Laboratory)
Leman Akoglu (CMU)
Danai Koutra (U Michigan)

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