Improving Graph Attention Networks with Large Margin-based Constraints

Understand

Graph Attention Networks (GATs) are the state-of-the-art neural architecture for representation learning with graphs.

• GATs learn attention functions that assign weights to nodes so that different nodes have different influences in the feature aggregation steps.
• In practice, however, induced attention functions are prone to over-fitting due to the increasing number of parameters and the lack of direct supervision on attention weights.
• GATs also suffer from over-smoothing at the decision boundary of nodes.