DiffWire: Inductive Graph Rewiring via the Lov\'asz Bound

Understand

Graph Neural Networks (GNNs) have been shown to achieve competitive results to tackle graph-related tasks, such as node and graph classification, link prediction and node and graph clustering in a variety of domains.

• Most GNNs use a message passing framework and hence are called MPNNs.
• Despite their promising results, MPNNs have been reported to suffer from over-smoothing, over-squashing and under-reaching.
• Graph rewiring and graph pooling have been proposed in the literature as solutions to address these limitations.