  Local Attention-guided Message Passing Mechanism (LAMP) adopted in Graph
Attention Networks (GATs) is designed to adaptively learn the importance of
neighboring nodes for better local aggregation on the graph, which can bring
the representations of similar neighbors closer effectively, thus showing
stronger discrimination ability. However, existing GATs suffer from a
significant discrimination ability decline in heterophilic graphs because the
high proportion of dissimilar neighbors can weaken the self-attention of the
central node, jointly resulting in the deviation of the central node from
similar nodes in the representation space. This kind of effect generated by
neighboring nodes is called the Distraction Effect (DE) in this paper. To
estimate and weaken the DE of neighboring nodes, we propose a Causally graph
Attention network for Trimming heterophilic graph (CAT). To estimate the DE,
since the DE are generated through two paths (grab the attention assigned to
neighbors and reduce the self-attention of the central node), we use Total
Effect to model DE, which is a kind of causal estimand and can be estimated
from intervened data; To weaken the DE, we identify the neighbors with the
highest DE (we call them Distraction Neighbors) and remove them. We adopt three
representative GATs as the base model within the proposed CAT framework and
conduct experiments on seven heterophilic datasets in three different sizes.
Comparative experiments show that CAT can improve the node classification
accuracy of all base GAT models. Ablation experiments and visualization further
validate the enhancement of discrimination ability brought by CAT. The source
code is available at https://github.com/GeoX-Lab/CAT.
