Different Statistical Perspectives for Understanding Generalisation in Graph Neural Networks
arXiv:2605.25452v1 Announce Type: cross Abstract: 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 analysis to drug discovery. However, there is limited mathematical understanding of the performance of GNNs. We discuss the various perspectives used to study statistical generalisation in GNNs. We identify three broad frameworks. The first approach, rooted in learning theory, relies on uniform convergence bounds and the complexity of the hypothesis class of specific GNN
The rapid deployment of Graph Neural Networks (GNNs) in diverse applications necessitates a stronger theoretical foundation for understanding their reliability and performance, especially concerning generalisation.
A deeper mathematical understanding of GNN generalisation is crucial for responsible AI development, allowing for more robust, predictable, and trustworthy AI systems across critical sectors.
This research contributes to moving GNN development from empirical experimentation towards more theoretically grounded approaches, potentially leading to more efficient and reliable model design.
- · AI researchers
- · GNN developers
- · Industries relying on GNNs (e.g., drug discovery, social network analysis)
Improved theoretical understanding of GNNs leads to more robust model development.
Enhanced reliability and predictability of GNNs allows for their deployment in higher-stakes applications.
Increased trust in GNNs could accelerate innovation and adoption in fields requiring complex graph analysis.
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Read at arXiv cs.LG