Random Probing for Structural Self-Interactions in Graph Neural Networks
In this work, we introduce DIAL, a message-passing layer that gives nodes access to graph structure through the diagonal of graph-derived operators.
ProofPaper ↗
Key points
- Graph Neural Networks (GNNs) fundamentally lack the ability to capture structural properties defined by node self-interactions, such as cycle counts, subgraph centrality, and heat kernel signatures.
- Our approach uses randomized probing to provide nodes with learnable, permutation equivariant access to diagonal entries.
- We prove that our approach can express important structural features, including cycle counts, subgraph centrality, and heat kernel signatures.
- Experiments on molecular property prediction, cycle counting, and pattern detection demonstrate the effectiveness of DIAL across a range of graph learning tasks.
Sources (1)
- [1]Random Probing for Structural Self-Interactions in Graph Neural NetworksarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 11:07 AM
In this work, we introduce DIAL, a message-passing layer that gives nodes access to graph structure through the diagonal of graph-derived operators.
Graph Neural Networks (GNNs) fundamentally lack the ability to capture structural properties defined by node self-interactions, such as cycle counts, subgraph centrality, and heat kernel signatures.
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