  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.
