Boosting the Cycle Counting Power of Graph Neural Networks with I$^2$-GNNs

Understand

Message Passing Neural Networks (MPNNs) are a widely used class of Graph Neural Networks (GNNs).

• The limited representational power of MPNNs inspires the study of provably powerful GNN architectures.
• However, knowing one model is more powerful than another gives little insight about what functions they can or cannot express.
• It is still unclear whether these models are able to approximate specific functions such as counting certain graph substructures, which is essential for applications in biology, chemistry and social network analysis.