Graph Neural Networks (GNNs) have shown remarkable success in graph representation learning. Unfortunately, current weight assignment schemes in standard GNNs, such as the calculation based on node degrees or pair-wise representations, can hardly be effective in processing the networks with heterophily, in which the connected nodes usually possess different labels or features. Existing heterophilic GNNs tend to ignore the modeling of heterophily of each edge, which is also a vital part in tackling the heterophily problem. In this paper, we firstly propose a heterophily-aware attention scheme and reveal the benefits of modeling the edge heterophily, i.e., if a GNN assigns different weights to edges according to different heterophilic types, it can learn effective local attention patterns, which enable nodes to acquire appropriate information from distinct neighbors. Then, we propose a novel Heterophily-Aware Graph Attention Network (HA-GAT) by fully exploring and utilizing the local distribution as the underlying heterophily, to handle the networks with different homophily ratios. To demonstrate the effectiveness of the proposed HA-GAT, we analyze the proposed heterophily-aware attention scheme and local distribution exploration, by seeking for an interpretation from their mechanism. Extensive results demonstrate that our HA-GAT achieves state-of-the-art performances on eight datasets with different homophily ratios in both the supervised and semi-supervised node classification tasks.

翻译：图神经网络在图表示学习领域取得了显著成功。然而，标准图神经网络中的权重分配方案（例如基于节点度或成对表示的计算）在处理异质性网络时往往难以奏效，因为此类网络中相连的节点通常具有不同的标签或特征。现有异质图神经网络倾向于忽略对每条边异质性的建模，而这正是解决异质性问题的关键环节。本文首先提出一种异质性感知注意力机制，并揭示了建模边异质性的优势：若图神经网络能根据不同的异质类型为边分配差异化权重，即可学习有效的局部注意力模式，使节点能够从不同邻居处获取适配信息。随后，我们通过充分挖掘和利用局部分布作为底层异质性，提出一种新型异质性感知图注意力网络，以处理具有不同同质性比率的网络。为验证所提网络的有效性，我们通过机理分析对异质性感知注意力机制与局部分布挖掘进行解析。大量实验结果表明，在监督和半监督节点分类任务中，我们的模型在八个具有不同同质性比率的数据集上均达到了最先进的性能水平。