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Top-K Structural Diversity Search in Large Networks

Summary: Introduces top-k structural diversity search in large networks, leveraging an incrementally refined upper bound on neighborhood connected components to prune the search space. Proposes an efficient framework with heuristic strategies, and validates on 13 real networks, demonstrating scalable, effective top-k discovery. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
10650
Venue
VLDB
Year
2013
Pagerank
4.7817823e-05
Overall Rank
7,268 | 49.44%
DOI
-

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Showing 6 of 6 cited papers.

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

Rank Cited Paper Year Venue Pagerank
7 Optimal Aggregation Algorithms for Middleware [Extended Abstract] 2001 PODS 0.0015496097
108 Truss Decomposition in Massive Networks 2012 VLDB 0.00048300163
1,096 Minimal Probing: Supporting Expensive Predicates for Top-k Queries 2002 SIGMOD 0.00014120512
1,201 SPARK: Top-k Keyword Query in Relational Databases 2007 SIGMOD 0.0001334371
1,208 Efficient Diversity-Aware Search 2011 SIGMOD 0.00013275712
1,445 Diversifying Top-K Results 2012 VLDB 0.00011945231
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