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: 13879
Venue: VLDB
Year: 2025
Pagerank: 4.1945683e-05
Overall Rank: 10,604 | 26.23%
DOI: 10.14778/3734839.3734862

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Rank	Cited Paper	Year	Venue	Pagerank
3,234	BigST: Linear Complexity Spatio-Temporal Graph Neural Network for Traffic Forecasting on Large-Scale Road Networks	2024	VLDB	7.3355287e-05

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