Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs
This addresses a limitation in GNNs for node classification in heterophilic networks, which is an incremental improvement over existing methods.
The paper tackles the problem of graph neural networks (GNNs) failing to generalize to heterophilic graphs where connected nodes have different labels, and shows that key designs like embedding separation and higher-order neighborhoods increase accuracy by up to 40% on synthetic and 27% on real networks with heterophily.
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.