Neighborhood-Preserving Graph Sparsification
Summary: Proposes a graph sparsification technique that preserves per-node neighborhood information to support neighborhood-dependent mining, learning and reachability tasks with good approximation guarantees. User-tunable size vs. information-loss tradeoff; experiments show ~40% average compression while retaining utility for node/graph classification and shortest-path approximations. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Abd Errahmane Kiouche (CNRS; National Institute of Applied Sciences of Lyon; University Claude Bernard Lyon 1)
- 2. Julien Baste (CNRS; Centrale Lille Institute; University of Lille)
- 3. Mohammed Haddad (CNRS; National Institute of Applied Sciences of Lyon; University Claude Bernard Lyon 1)
- 4. Hamida Seba (CNRS; National Institute for Applied Sciences of Lyon; University Claude Bernard Lyon 1)
- 5. Angela Bonifati (CNRS; Institut Universitaire de France; University Claude Bernard Lyon 1)
BibTeX Citation
@article{kiouche_vldb24,
title = {{Neighborhood-Preserving Graph Sparsification}},
author = {Kiouche, Abd Errahmane and Baste, Julien and Haddad, Mohammed and Seba, Hamida and Bonifati, Angela},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {13},
pages = {4853--4866},
doi = {10.14778/3704965.3704988},
url = {https://doi.org/10.14778/3704965.3704988},
year = {2024}
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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 |
|---|---|---|---|---|
| 1,163 | Local Graph Sparsification for Scalable Clustering | 2011 | SIGMOD | 0.00011865557 |
| 5,415 | Making Graphs Compact by Lossless Contraction | 2021 | SIGMOD | 6.2255551e-05 |
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