Database Paper Browser

Back to papers

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)

Paper ID
13727
Venue
VLDB
Year
2024
Pagerank
4.1945683e-05
Overall Rank
11,138 | 22.52%
DOI
10.14778/3704965.3704988

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank Citing Paper Year Venue Pagerank
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
777 Local Graph Sparsification for Scalable Clustering 2011 SIGMOD 0.0001679862
4,836 Making Graphs Compact by Lossless Contraction 2021 SIGMOD 5.8896897e-05
Previous Page 1 / 1 Next

Semantically Similar Papers