A survey paper on graph rewiring techniques for Graph Neural Networks (GNNs) has been accepted at IJCAI 2026. The paper addresses the challenges of over-squashing and over-smoothing in GNNs, which hinder information flow and performance. It proposes graph rewiring as a method to improve GNNs by modifying their topology and opens a discussion on the necessity and attribution of improvements from such interventions. AI
IMPACT This survey provides a structured overview of graph rewiring techniques, potentially guiding future research and development in GNNs for complex data structures.
RANK_REASON The cluster contains a survey paper accepted at a major AI conference, detailing research on Graph Neural Networks. [lever_c_demoted from research: ic=1 ai=1.0]
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- CentraleSupélec
- Davide Buscaldi
- Fragkiskos D. Malliaros
- Graph Neural Networks
- Hugo Attali
- IJCAI 2026
- Nathalie Pernelle
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