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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
- 11561
- Venue
- VLDB
- Year
- 2017
- Pagerank
- 5.9628488e-05
- Overall Rank
- 4,727 | 67.15%
- DOI
-
-
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 11 of 11 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 3,274 |
Global Reinforcement of Social Networks: The Anchored Coreness Problem |
2020 |
SIGMOD |
7.2886717e-05 |
| 3,323 |
Hierarchical Core Maintenance on Large Dynamic Graphs |
2021 |
VLDB |
7.2170169e-05 |
| 4,147 |
Scaling Up k-Clique Densest Subgraph Detection |
2023 |
SIGMOD |
6.4060893e-05 |
| 4,391 |
When Engagement Meets Similarity: Efficient (k,r)-Core Computation on Social Networks |
2017 |
VLDB |
6.2190075e-05 |
| 4,737 |
Cohesive Subgraph Search over Big Heterogeneous Information Networks: Applications, Challenges, and Solutions |
2021 |
SIGMOD |
5.9529218e-05 |
| 6,528 |
Fast Algorithms for Core Maximization on Large Graphs |
2022 |
VLDB |
5.0198578e-05 |
| 6,588 |
Efficient Progressive Minimum k-Core Search |
2020 |
VLDB |
4.9972607e-05 |
| 8,375 |
From Anomaly Detection to Rumour Detection using Data Streams of Social Platforms |
2019 |
VLDB |
4.5278191e-05 |
| 10,246 |
Anchored Maximum Communities over Large Directed Graphs |
2026 |
VLDB |
4.1905499e-05 |
| 10,375 |
A Local Search Approach to Efficient (k, p)-Core Maintenance |
2025 |
SIGMOD |
4.1905499e-05 |
| 10,702 |
Effective and Efficient Community Search for Complex Network Semantics Capture: From Coarse-Grain to Fine-Grain |
2025 |
VLDB |
4.1905499e-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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