The Expressive GNN Series
Introducing the MPNN architecture with PyTorch Geometric to connect the dots for a theoretical analysis of Graph Neural Network models
Graph Neural Networks (GNNs) are powerful architectures designed to model and analyze data structured as graphs. These models effectively capture patterns within such interconnected information, enabling a range of downstream tasks, including node classification, link prediction, and graph regression.
In the previous article of this series, we introduced the idea of graph isomorphism, which is essential to clarifying the concept of distinct or equivalent relational structures.
Following the graph isomorphism principle, we can set the requirements for any machine learning model that operates on graphs:
- Produce identical representations for isomorphic graphs.
- Disambiguate graphs characterized by distinct relational structures.