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A CPU-GPU Hybrid Labelling Algorithm for Massive Shortest Distance Queries on Road Networks
Summary: G2H is a CPU-GPU hybrid hop-labeling method for rapidly rebuilding road-network distance indexes under frequent updates, combining hybrid graph partitioning, optimized ordering, and parallel label pruning. It constructs indexes for 6M-vertex networks in under a minute and serves hundreds of millions of queries per second.
(summarized by gpt-5.6-luna on Jul 24 2026)
Paper ID
h8e9a15bb0d33fb80
Venue
VLDB
Year
2025
Pagerank
4.9769913e-05
Overall Rank
11,455 | 23.01%
DOI
10.14778/3712221.3712241
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(CC BY-NC-ND 4.0)
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@article{li_vldb25,
title = {{A CPU-GPU Hybrid Labelling Algorithm for Massive Shortest Distance Queries on Road Networks}},
author = {Li, Jiajia and Chen, Yongzhi and Zhang, Mengxuan and Li, Lei},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {3},
pages = {770--783},
doi = {10.14778/3712221.3712241},
url = {https://doi.org/10.14778/3712221.3712241},
year = {2025}
}
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Outgoing Citations (Sorted by Pagerank)
Showing 22 of 22 cited papers.
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Pagerank
3
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2010
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Fast Exact Shortest-Path Distance Queries on Large Networks by Pruned Landmark Labeling
2013
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TEDI: Efficient Shortest Path Query Answering on Graphs
2010
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An Experimental Study on Hub Labeling based Shortest Path Algorithms
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When Hierarchy Meets 2-Hop-Labeling: Efficient Shortest Distance Queries on Road Networks
2018
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9.9906562e-05
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Efficient Shortest Path Index Maintenance on Dynamic Road Networks with Theoretical Guarantees
2020
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2,190
Scaling Distance Labeling on Small-World Networks
2019
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2,944
P2H: Efficient Distance Querying on Road Networks by Projected Vertex Separators
2021
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7.8277412e-05
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Effective Caching of Shortest Paths for Location-Based Services
2012
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6.9219828e-05
4,491
Scaling Up Distance Labeling on Graphs with Core-Periphery Properties
2020
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5,303
Parallel Personalized PageRank on Dynamic Graphs
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5,731
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2021
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6,087
Progressive Top-K Nearest Neighbors Search in Large Road Networks
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BatchHL: Answering Distance Queries on Batch-Dynamic Networks at Scale
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Planting Trees for scalable and efficient Canonical Hub Labeling
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