3 papers
stat.ME2026
Different Statistical Perspectives for Understanding Generalisation in Graph Neural Networks
Nil Ayday, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar
Graph Neural Networks (GNN) are currently the most popular approach for learning and prediction on graph-structured data and are deployed in various fields, from social network ana…
stat.ML2026
Gaussian Process Limit Reveals Structural Benefits of Graph Transformers
Nil Ayday, Lingchu Yang, Debarghya Ghoshdastidar
Graph transformers are the state-of-the-art for learning from graph-structured data and are empirically known to avoid several pitfalls of message-passing architectures. However, t…
stat.ML2026
Exact Generalisation Error Exposes Benchmarks Skew Graph Neural Networks Success (or Failure)
Nil Ayday, Mahalakshmi Sabanayagam, Debarghya Ghoshdastidar
Graph Neural Networks (GNNs) have become the standard method for learning from networks across fields ranging from biology to social systems, yet a principled understanding of what…