Scaffold framework sparsifies GNNs using support graph theory

Researchers have developed Scaffold, a new framework for sparsifying graph neural networks (GNNs) that utilizes support graph theory. This method aims to reduce the computational and memory costs associated with GNNs by removing edges while preserving crucial communication paths. Scaffold controls dilation and congestion to maintain performance, achieving competitive or improved results across various benchmarks using significantly fewer edges and less memory. AI

IMPACT Reduces computational and memory costs for GNNs, potentially enabling larger or more complex graph-based AI models.

RANK_REASON This is a research paper detailing a new method for graph neural networks. [lever_c_demoted from research: ic=1 ai=1.0]

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• alphaXiv
• arXiv
• CatalyzeX
• congestion
• DagsHub
• dilation
• Gotit.pub
• graph neural networks
• Hugging Face
• ScienceCast
• Siddhartha Shankar Das
• support graph theory

• paper
• infra

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