Graph Convolutional Network (GCN) has shown remarkable potential of exploring graph representation. However, the GCN aggregating mechanism fails to generalize to networks with heterophily where most nodes have neighbors from different classes, which commonly exists in real-world networks. In order to make the propagation and aggregation mechanism of GCN suitable for both homophily and heterophily (or even their mixture), we introduce block modeling into the framework of GCN so that it can realize "block-guided classified aggregation", and automatically learn the corresponding aggregation rules for neighbors of different classes. By incorporating block modeling into the aggregation process, GCN is able to aggregate information from homophilic and heterophilic neighbors discriminately according to their homophily degree. We compared our algorithm with state-of-art methods which deal with the heterophily problem. Empirical results demonstrate the superiority of our new approach over existing methods in heterophilic datasets while maintaining a competitive performance in homophilic datasets.

本文介绍了将块建模引入图卷积网络（Graph Convolutional Network，GCN）框架中，以实现块引导分类聚合，从而使GCN的传播和聚合机制适用于同质性和异质性网络。通过将块建模纳入聚合过程，GCN能够根据其同质性程度有区别地聚合同质性和异质性邻居的信息，并在异质性数据集上表现出优异的性能。

基于区块建模引导的图卷积神经网络