  Graph neural networks have become the standard approach for dealing with
learning problems on graphs. Among the different variants of graph neural
networks, graph attention networks (GATs) have been applied with great success
to different tasks. In the GAT model, each node assigns an importance score to
its neighbors using an attention mechanism. However, similar to other graph
neural networks, GATs aggregate messages from nodes that belong to different
classes, and therefore produce node representations that are not well separated
with respect to the different classes, which might hurt their performance. In
this work, to alleviate this problem, we propose a new technique that can be
incorporated into any graph attention model to encourage higher attention
scores between nodes that share the same class label. We evaluate the proposed
method on several node classification datasets demonstrating increased
performance over standard baseline models.
