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Simple Random Node Sampling outperforms full-graph training for GNNs

By PulseAugur Editorial · [2 sources] ·

Researchers have found that a simple Random Node Sampling (RNS) method for training Graph Neural Networks (GNNs) can match or exceed the performance of full-graph training. This surprising result holds true across numerous datasets, achieving better outcomes with significantly less computational time and memory. The study's analysis suggests that RNS acts as an implicit regularizer, effectively minimizing a combination of sampled loss and gradient variance, thereby offering a theoretically sound approach for scalable GNN training. AI

IMPACT This research offers a more efficient and effective method for training Graph Neural Networks, potentially accelerating their adoption in various applications.

RANK_REASON The cluster contains an academic paper detailing a new method for training machine learning models.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Clement Wang, Antoine Vialle, Robin Vaysse, Thomas Bonald ·

    Implicit Regularization of Mini-Batch Training in Graph Neural Networks

    arXiv:2605.22480v1 Announce Type: new Abstract: Mini-batch training of Graph Neural Networks (GNNs) is fundamentally different from training on i.i.d. data: sampling a subgraph alters the topology and introduces boundary effects, leading prior work to develop structure-aware samp…

  2. arXiv cs.AI TIER_1 English(EN) · Thomas Bonald ·

    Implicit Regularization of Mini-Batch Training in Graph Neural Networks

    Mini-batch training of Graph Neural Networks (GNNs) is fundamentally different from training on i.i.d. data: sampling a subgraph alters the topology and introduces boundary effects, leading prior work to develop structure-aware samplers that preserve local connectivity and reduce…

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