TopGQ: Fast GNN Post-Training Quantization Leveraging Topology Information

arXiv:2608.30394 · cs.LG, cs.AI · Submitted 2026-08-31 · Read on arXiv

cs.LG, cs.AI

Submitted: 2026-08-31

Updated: 2026-08-31

Code: https://github.com/meowrowan/TopGQ

Terminology

• Quantization — 117× in this paper · explained in 12 episodes
• Accuracy — 24× in this paper · explained in 1 episode
• Aggregation — 15× in this paper · explained in 1 episode
• GNNs — 10× in this paper · explained in 2 episodes
• Calibration — 7× in this paper · explained in 7 episodes
• Adjacency Matrix — 4× in this paper · explained in 1 episode
• Ablation — 3× in this paper · explained in 1 episode
• Ablation study — 3× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Benchmark — 2× in this paper · explained in 2 episodes
• Computational overhead — 2× in this paper · explained in 2 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Diffusion Models — 2× in this paper · explained in 6 episodes
• Edge Devices — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Quantization-Aware Training — 2× in this paper · explained in 3 episodes
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Graph Neural Networks (GNNs) — 1× in this paper · explained in 7 episodes
• Heterogeneous Graph — 1× in this paper · explained in 2 episodes
• Personalization — 1× in this paper · explained in 2 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes

Sources

• NeutronStream: A Dynamic GNN Training Framework with Sliding Window for Graph Streams
• ReInc: Scaling Training of Dynamic Graph Neural Networks
• OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
• InkStream: Real-time GNN Inference on Streaming Graphs via Incremental Update

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