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CoRe-GNN advances graph neural network training with dual message passing

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced CoRe-GNN, a novel approach to training graph neural networks (GNNs) on large graphs. This method addresses memory limitations by performing two types of message passing in parallel: a coarsened inter-cluster term for long-range structure and a local intra-cluster term for node-specific details. CoRe-GNN aims to combine the benefits of graph coarsening and Cluster-GCN, outperforming these baselines on various node classification benchmarks, including those with long-range dependencies, while maintaining memory efficiency. AI

IMPACT CoRe-GNN offers a more memory-efficient and accurate method for training graph neural networks on large datasets, potentially improving performance in applications like recommendation systems and social network analysis.

RANK_REASON The cluster describes a new research paper detailing a novel method for graph neural networks.

Read on Hugging Face Daily Papers →

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CoRe-GNN advances graph neural network training with dual message passing

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Antonin Joly, Nicolas Keriven, Aline Roumy ·

    CoRe-GNN: Multilevel Message passing on Coarsened graphs

    arXiv:2608.02128v1 Announce Type: new Abstract: Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several existing scalable approaches can be written as structured modifications of the G…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    CoRe-GNN: Multilevel Message passing on Coarsened graphs

    Training Graph Neural Networks on large graphs is challenged by the memory cost of storing all node representations across layers. We show that several existing scalable approaches can be written as structured modifications of the GNN propagation matrix, providing a unified persp…

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