Algorithm and System Co-design for Efficient Subgraph-based Graph Representation Learning
Summary: Co-design of learning and system for scalable SGRL using walk-based subgraph decomposition to reuse walks and cut extraction redundancy. SUREL scales to millions of nodes/edges, delivering ~10x speed-up over SGRL baselines and up to 50% accuracy gains vs canonical GNNs. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Haoteng Yin
- 2. Muhan Zhang
- 3. Yanbang Wang
- 4. Jianguo Wang
- 5. Pan Li
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,039 | SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning | 2023 | VLDB | 5.2413564e-05 |
| 7,749 | GENTI: GPU-powered Walk-based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs | 2024 | VLDB | 4.6610143e-05 |
| 9,872 | Substructure-aware Log Anomaly Detection | 2025 | VLDB | 4.2667743e-05 |
| 10,011 | A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness | 2026 | SIGMOD | 4.1945683e-05 |
| 10,887 | Towards Ideal Temporal Graph Neural Networks: Evaluations and Conclusions after 10,000 GPU Hours | 2025 | VLDB | 4.1945683e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 278 | AliGraph: A Comprehensive Graph Neural Network Platform | 2019 | VLDB | 0.00029230623 |
| 1,329 | AGL: A Scalable System for Industrial-purpose Graph Machine Learning | 2020 | VLDB | 0.00012561848 |
| 2,177 | Accelerating Large Scale Real-Time GNN Inference using Channel Pruning | 2021 | VLDB | 9.359876e-05 |
| 3,986 | G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs | 2020 | VLDB | 6.5611714e-05 |
| 4,165 | Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization | 2021 | VLDB | 6.3921956e-05 |
Previous
Page 1 / 1
Next