  Graph Neural Networks (GNNs) often assume strong homophily for graph
classification, seldom considering heterophily, which means connected nodes
tend to have different class labels and dissimilar features. In real-world
scenarios, graphs may have nodes that exhibit both homophily and heterophily.
Failing to generalize to this setting makes many GNNs underperform in graph
classification. In this paper, we address this limitation by identifying three
effective designs and develop a novel GNN architecture called IHGNN (short for
Incorporating Heterophily into Graph Neural Networks). These designs include
the combination of integration and separation of the ego- and
neighbor-embeddings of nodes, adaptive aggregation of node embeddings from
different layers, and differentiation between different node embeddings for
constructing the graph-level readout function. We empirically validate IHGNN on
various graph datasets and demonstrate that it outperforms the state-of-the-art
GNNs for graph classification.
