Stanford CS224W: ML with Graphs | 2021 | Lecture 9.2 – Designing the Most Powerful GNNs

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Jure Leskovec
Computer Science, PhD

In this lecture, we aim to design a maximally expressive GNN model. Our key insight is that a maximally expressive GNN should use the injective neighbor aggregation function that maps different neighboring node features into different embeddings. We first demonstrate neighbor aggregation functions used by popular GNN models (e.g., Graph Convolutional Networks and GraphSAGE) are not injective. We then design injective neighbor aggregation functions using neural networks and arrive at a Graph Isomorphism Network, the most powerful GNN model.

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