mlsanityData to 14 Oct 2026

TopGQ: Fast GNN Post-Training Quantization Leveraging Topology Information

arXiv 2608.30394 ↗First posted 31 Aug 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

Existing GNN quantization methods suffer from considerable quantization overhead, which severely limits their practical usage in real-world scenarios. To this end, we present TopGQ, an accurate post-training GNN quantization framework, alleviating redundant quantization overhead. We propose dual-axis scale absorption, which enables activation quantization along both the outer and inner dimensions by merging one into the adjacency matrix. On top of that, we introduce TopPIN, a proxy for nodes' local structure, and use it to group nodes with similar topology during quantization. Experimental results show that TopGQ reduces quantization time by an order of magnitude while preserving accuracy.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 31 Aug 2026.

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22 of 34 references.

References · 22

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10 more
  1. 2020Degree-Quant: Quantization-Aware Training for Graph Neural Networkscited by 96
  2. 2020Binary Graph Neural Networkscited by 34
  3. 2022Monolith: Real Time Recommendation System With Collisionless Embedding Tablecited by 30
  4. 2020SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantizationcited by 27
  5. 2021Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networkscited by 25
  6. 2023NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streamscited by 6
  7. 2023Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagationcited by 5
  8. 2021Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendationcited by 3
  9. 2021Prioritizing Original News on Facebookcited by 2
  10. 2023InkStream: Real-time GNN Inference on Streaming Graphs via Incremental Updatecited by 2

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  2. 2023Tango: rethinking quantization for graph neural network training on GPUs
  3. 2021VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization
  4. 2023GHN-QAT: Training Graph Hypernetworks to Predict Quantization-Robust Parameters of Unseen Limited Precision Neural Networks
  5. 2022QFT: Post-training quantization via fast joint finetuning of all degrees of freedom
  6. 2022Post-training Quantization for Neural Networks with Provable Guarantees
  7. 2025Efficient Mixed Precision Quantization in Graph Neural Networks
  8. 2021PTQ-SL: Exploring the Sub-layerwise Post-training Quantization