Expanding Reverse Nearest Neighbors
Summary: ERNN: choose up to k edge weight reductions to maximize a facility's reverse nearest neighbor set; problem is NP-hard, APX-hard, non‑monotone and non‑submodular. Propose greedy heuristics that prune edges and cut per‑edge cost, giving orders‑of‑magnitude speedups and large RNN gains. (summarized by gpt-5-mini on Feb 09 2026)
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BibTeX Citation
@article{li_vldb24,
title = {{Expanding Reverse Nearest Neighbors}},
author = {Li, Wentao and Cai, Maolin and Gao, Min and Wen, Dong and Qin, Lu and Wang, Wei},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {4},
pages = {630--642},
doi = {10.14778/3636218.3636220},
url = {https://doi.org/10.14778/3636218.3636220},
year = {2024}
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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 |
|---|---|---|---|---|
| 545 | Influence Sets Based on Reverse Nearest Neighbor Queries | 2000 | SIGMOD | 0.00016607433 |
| 3,029 | Efficient Method for Maximizing Bichromatic Reverse Nearest Neighbor | 2009 | VLDB | 7.737708e-05 |
| 4,906 | Diversified Top-k Route Planning in Road Network | 2022 | VLDB | 6.3616468e-05 |
| 5,336 | Efficient Shortest Path Counting on Large Road Networks | 2022 | VLDB | 6.1747873e-05 |
| 5,513 | Big Graphs: Challenges and Opportunities | 2022 | VLDB | 6.0986544e-05 |
| 6,107 | Progressive Top-K Nearest Neighbors Search in Large Road Networks | 2020 | SIGMOD | 5.8852347e-05 |
| 6,548 | Fast Algorithms for Core Maximization on Large Graphs | 2022 | VLDB | 5.7507029e-05 |
| 6,830 | Maximizing Bichromatic Reverse Spatial and Textual k Nearest Neighbor Queries | 2016 | VLDB | 5.6691409e-05 |
| 7,515 | Public Transport Planning: When Transit Network Connectivity Meets Commuting Demand | 2021 | SIGMOD | 5.5049463e-05 |
| 9,093 | Noticeable Network Delay Minimization via Node Upgrades | 2018 | VLDB | 5.2283159e-05 |
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