Graph Neural Networks (GNNs) have achieved remarkable success in various
graph mining tasks by aggregating information from neighborhoods for
representation learning. The success relies on the homophily assumption that
nearby nodes exhibit similar behaviors, while it may be violated in many
real-world graphs. Recently, heterophilous graph neural networks (HeterGNNs)
have attracted increasing attention by modifying the neural message passing
schema for heterophilous neighborhoods. However, they suffer from insufficient
neighborhood partition and heterophily modeling, both of which are critical but
challenging to break through. To tackle these challenges, in this paper, we
propose heterophilous distribution propagation (HDP) for graph neural networks.
Instead of aggregating information from all neighborhoods, HDP adaptively
separates the neighbors into homophilous and heterphilous parts based on the
pseudo assignments during training. The heterophilous neighborhood distribution
is learned with orthogonality-oriented constraint via a trusted prototype
contrastive learning paradigm. Both the homophilous and heterophilous patterns
are propagated with a novel semantic-aware message passing mechanism. We
conduct extensive experiments on 9 benchmark datasets with different levels of
homophily. Experimental results show that our method outperforms representative
baselines on heterophilous datasets.

本研究提出了异质分布传播（HDP）用于图神经网络，通过在训练期间基于伪指派将邻居自适应分为同质和异质部分，通过可信原型对比学习范式的正交性约束学习异质邻居分布，并通过一种新颖的语义感知信息传递机制传播同质和异质模式，实验证明该方法在异质数据集上优于代表性基线方法。