Fast Network K-function-based Spatial Analysis
Summary: Proposes count augmentation (CA) and neighbor sharing (NS) to accelerate network K-function computation on spatial networks. Integrates shortest path sharing (ASPS) to reduce worst-case complexity, delivering up to 165.85x speedups over prior methods. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tsz Nam Chan (Hong Kong Baptist University)
- 2. Leong Hou U (University of Macau)
- 3. Yun Peng (Guangdong University of Technology)
- 4. Byron Choi (Hong Kong Baptist University)
- 5. Jianliang Xu (Hong Kong Baptist University)
BibTeX Citation
@article{chan_vldb22,
title = {{Fast Network K-function-based Spatial Analysis}},
author = {Chan, Tsz Nam and U, Leong Hou and Peng, Yun and Choi, Byron and Xu, Jianliang},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {11},
pages = {2853--2866},
doi = {10.14778/3551793.3551836},
url = {https://doi.org/10.14778/3551793.3551836},
year = {2022}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,061 | Large Language Models for Spatial Analysis Queries | 2025 | VLDB | 5.093636e-05 |
| 11,213 | LION: Fast and High-Resolution Network Kernel Density Visualization | 2024 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 17 of 17 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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