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GraphMuon optimizer accelerates Graph Neural Network training

By PulseAugur Editorial · [1 sources] ·

Researchers have developed GraphMuon, a novel optimizer designed to improve the training efficiency of Graph Neural Networks (GNNs). Unlike standard optimizers like AdamW that use coordinate-wise updates, GraphMuon adapts matrix-aware optimization techniques to account for the specific geometry of GNN weight matrices influenced by graph-propagated features. This approach leads to significant speedups in reaching target validation loss on large-scale OGB benchmarks, outperforming AdamW by up to 2.42x in time-to-target speedups across various GNN tasks. AI

IMPACT GraphMuon's demonstrated speedups could significantly reduce training times for GNNs, enabling faster iteration and deployment of graph-based AI models.

RANK_REASON The cluster contains a research paper detailing a new optimization technique for Graph Neural Networks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

GraphMuon optimizer accelerates Graph Neural Network training

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The cluster contains a research paper detailing a new optimization technique for Graph Neural Networks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Guy Hadad, Haggai Roitman, Moshe Eliasof, Bracha Shapira ·

    Module-Aware Optimization for Graph Neural Networks

    arXiv:2611.08379v1 Announce Type: new Abstract: Different optimizers impose different geometries on parameter updates. Adaptive methods such as Adam and AdamW use coordinate-wise updates, while matrix-aware optimizers such as Muon utilize weight-matrix structure through orthogona…

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