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
- 2. Julien Baste
- 3. Mohammed Haddad
- 4. Hamida Seba
- 5. Angela Bonifati
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 773 | Local Graph Sparsification for Scalable Clustering | 2011 | SIGMOD | 0.00016788213 |
| 5,030 | Making Graphs Compact by Lossless Contraction | 2021 | SIGMOD | 5.7445683e-05 |
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