When Engagement Meets Similarity: Efficient (k,r)-Core Computation on Social Networks
Summary: Defines (k,r)-cores combining k-core engagement constraints with pairwise attribute similarity, yielding cohesive social subgraphs. For NP-hard maximal enumeration and maximum-core search, it introduces pruning, search orders, and a novel (k,k′)-core size bound. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Fan Zhang (East China Normal University; University of Technology Sydney)
- 2. Ying Zhang (University of Technology Sydney)
- 3. Lu Qin (University of Technology Sydney)
- 4. Wenjie Zhang (University of New South Wales)
- 5. Xuemin Lin (East China Normal University; University of New South Wales)
BibTeX Citation
@article{zhang_vldb17,
title = {{When Engagement Meets Similarity: Efficient (k,r)-Core Computation on Social Networks}},
author = {Zhang, Fan and Zhang, Ying and Qin, Lu and Zhang, Wenjie and Lin, Xuemin},
journal = {PVLDB},
series = {{VLDB} '17},
volume = {10},
number = {10},
pages = {998--1009},
doi = {10.14778/3115404.3115412},
url = {https://doi.org/10.14778/3115404.3115412},
year = {2017}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 696 | Effective Community Search for Large Attributed Graphs | 2016 | VLDB | 0.00014671367 |
| 1,405 | Effective Community Search over Large Spatial Graphs | 2017 | VLDB | 0.00010745719 |
| 2,072 | Truss Decomposition of Probabilistic Graphs: Semantics and Algorithms | 2016 | SIGMOD | 9.0668141e-05 |
| 2,690 | A Model-based Approach to Attributed Graph Clustering | 2012 | SIGMOD | 8.1126575e-05 |
| 4,597 | OLAK: An Efficient Algorithm to Prevent Unraveling in Social Networks | 2017 | VLDB | 6.4979622e-05 |
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