GraphSparseNet: a Novel Method for Large Scale Traffic Flow Prediction

Summary: GraphSparseNet (GSNet): GNN forecasting using a Feature Extractor and Relational Compressor to reduce graph complexity to linear time/space. 3.51× training speedup vs. SOTA linear baselines on real traffic data while preserving accuracy, offering a scalable alternative to sparsification/decomposition/kernel fixes. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 14067
Venue: VLDB
Year: 2025
Pagerank: 5.093636e-05
Overall Rank: 10,862 | 25.48%
DOI: 10.14778/3734839.3734862

Incoming Non-self Citations Over Time

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Authors

1. Weiyang Kong (Sun Yat-Sen University)
2. Kaiqi Wu (Sun Yat-Sen University)
3. Sen Zhang (Sun Yat-Sen University)
4. Yubao Liu (Sun Yat-Sen University)

BibTeX Citation

@article{kong_vldb25,
        title = {{GraphSparseNet: a Novel Method for Large Scale Traffic Flow Prediction}},
        author = {Kong, Weiyang and Wu, Kaiqi and Zhang, Sen and Liu, Yubao},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {7},
        pages = {2295--2307},
        doi = {10.14778/3734839.3734862},
        url = {https://doi.org/10.14778/3734839.3734862},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

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Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
4,038	BigST: Linear Complexity Spatio-Temporal Graph Neural Network for Traffic Forecasting on Large-Scale Road Networks	2024	VLDB	6.9431109e-05

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