Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of applications, recent studies exposed important shortcomings in their ability to capture the structure of the underlying graph. It has been shown that the expressive power of standard GNNs is bounded by the Weisfeiler-Leman (WL) graph isomorphism test, …