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Survey paper on GNN graph rewiring accepted at IJCAI 2026

By PulseAugur Editorial · [1 sources] ·

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]

Read on Mastodon — fosstodon.org →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Survey paper on GNN graph rewiring accepted at IJCAI 2026

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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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COVERAGE [1]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    🚀 Excited to share that a survey paper from our RCLN team has been accepted at IJCAI 2026! This work has been done in collaboration with CentraleSupélec. "Graph

    🚀 Excited to share that a survey paper from our RCLN team has been accepted at IJCAI 2026! This work has been done in collaboration with CentraleSupélec. "Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey" By Hugo Attali, Nathalie Pernelle, Davide Bus…

    LINKS arxiv.org/…/2411.17429

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