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OLAK: An Efficient Algorithm to Prevent Unraveling in Social Networks
Summary: OLAK uses an onion-layer (onion-peeling) structure to solve anchored k-core on large graphs, constraining anchors to onion layers and enabling aggressive pruning. Experiments on 10 real networks show scalable, efficient performance on general graphs, outperforming bounded-treewidth methods.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 11560
- Venue
- VLDB
- Year
- 2017
- Pagerank
- 5.968586e-05
- Overall Rank
- 4,728 | 67.11%
- DOI
-
-
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 11 of 11 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 3,273 |
Global Reinforcement of Social Networks: The Anchored Coreness Problem |
2020 |
SIGMOD |
7.2956944e-05 |
| 3,321 |
Hierarchical Core Maintenance on Large Dynamic Graphs |
2021 |
VLDB |
7.223972e-05 |
| 4,145 |
Scaling Up k-Clique Densest Subgraph Detection |
2023 |
SIGMOD |
6.4122551e-05 |
| 4,394 |
When Engagement Meets Similarity: Efficient (k,r)-Core Computation on Social Networks |
2017 |
VLDB |
6.224902e-05 |
| 4,743 |
Cohesive Subgraph Search over Big Heterogeneous Information Networks: Applications, Challenges, and Solutions |
2021 |
SIGMOD |
5.9586508e-05 |
| 6,530 |
Fast Algorithms for Core Maximization on Large Graphs |
2022 |
VLDB |
5.0246792e-05 |
| 6,588 |
Efficient Progressive Minimum k-Core Search |
2020 |
VLDB |
5.0020648e-05 |
| 8,376 |
From Anomaly Detection to Rumour Detection using Data Streams of Social Platforms |
2019 |
VLDB |
4.5321619e-05 |
| 10,246 |
Anchored Maximum Communities over Large Directed Graphs |
2026 |
VLDB |
4.1945683e-05 |
| 10,363 |
A Local Search Approach to Efficient (k, p)-Core Maintenance |
2025 |
SIGMOD |
4.1945683e-05 |
| 10,694 |
Effective and Efficient Community Search for Complex Network Semantics Capture: From Coarse-Grain to Fine-Grain |
2025 |
VLDB |
4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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