The Expressive GNN Sequence
Introducing the MPNN structure with PyTorch Geometric to attach the dots for a theoretical evaluation of Graph Neural Community fashions
Graph Neural Networks (GNNs) are highly effective architectures designed to mannequin and analyze knowledge structured as graphs. These fashions successfully seize patterns inside such interconnected info, enabling a spread of downstream duties, together with node classification, hyperlink prediction, and graph regression.
Within the earlier article of this sequence, we launched the concept of graph isomorphism, which is crucial to clarifying the idea of distinct or equal relational buildings.
Following the graph isomorphism precept, we are able to set the necessities for any machine studying mannequin that operates on graphs:
- Produce equivalent representations for isomorphic graphs.
- Disambiguate graphs characterised by distinct relational buildings.