Graph Neural Networks Applications Across Domains: All Insights You Need
Graph neural networks have moved from a niche representation-learning technique to the default model class wherever data carry relational structure. The interesting question is no longer whether message passing helps on a given dataset, but where graph structure earns its computational cost and where it does not. This survey organises the field around a single design space, derives the spectral and spatial formulations from shared first principles, and connects expressive power to the Weisfeiler-Leman hierarchy with explicit statements of what current architectures can and cannot separate. Against that methodological backbone we examine twelve application domains, among them recommendatio...
| Authors | Abderaouf Bahi |
|---|---|
| Primary category | cs.LG |
| Published | 25 June 2026 |
| Last updated at source | 25 June 2026 |
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