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.

翻译：图神经网络（GNNs）在图分类中通常假设强同质性，很少考虑异质性，即相连节点倾向于具有不同的类别标签和不相似的特征。在现实场景中，图的节点可能同时表现出同质性和异质性。未能泛化到这种场景导致许多GNN在图分类中表现不佳。本文通过识别三种有效设计来解决这一局限，并开发了一种新颖的GNN架构——IHGNN（Incorporating Heterophily into Graph Neural Networks的缩写）。这些设计包括：节点自身嵌入与邻居嵌入的整合与分离相结合、不同层节点嵌入的自适应聚合、以及构建图级读出函数时对不同节点嵌入的区分。我们在多种图数据集上对IHGNN进行了实证验证，结果表明其在图分类任务上优于当前最先进的GNNs。