mlsanityData to 14 Oct 2026

Graph Entropy Minimization for Semi-supervised Node Classification

arXiv 2305.19502 ↗First posted 31 May 2023cs.LGcited by 0

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Abstract (arXiv)

Node classifiers are required to comprehensively reduce prediction errors, training resources, and inference latency in the industry. However, most graph neural networks (GNN) concentrate only on one or two of them. The compromised aspects thus are the shortest boards on the bucket, hindering their practical deployments for industrial-level tasks. This work proposes a novel semi-supervised learning method termed Graph Entropy Minimization (GEM) to resolve the three issues simultaneously. GEM benefits its one-hop aggregation from massive uncategorized nodes, making its prediction accuracy comparable to GNNs with two or more hops message passing. It can be decomposed to support stochastic training with mini-batches of independent edge samples, achieving extremely fast sampling and space-saving training. While its one-hop aggregation is faster in inference than deep GNNs, GEM can be further accelerated to an extreme by deriving a non-hop classifier via online knowledge distillation. Thus, GEM can be a handy choice for latency-restricted and error-sensitive services running on resource-constraint hardware. Code is available at https://github.com/cf020031308/GEM.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 36 references that are in the map. First posted on arXiv 31 May 2023.

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31 of 36 references.

References · 31

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19 more
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