Revisiting the Stop-and-Stare Algorithms for Influence Maximization
Summary: Rigorous theoretical and empirical study of SSA and D-SSA for influence maximization, with comparison to TIM+ and IMM. It uncovers inaccuracies in prior results, reproduces original experiments, and introduces SSA-Fix to restore claimed approximation guarantees, highlighting scaling opportunities with guarantees. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Keke Huang (Nanyang Technological University)
- 2. Sibo Wang (Nanyang Technological University)
- 3. Glenn Bevilacqua (University of British Columbia)
- 4. Xiaokui Xiao (Nanyang Technological University)
- 5. Laks V.S. Lakshmanan (University of British Columbia)
BibTeX Citation
@article{huang_vldb17,
title = {{Revisiting the Stop-and-Stare Algorithms for Influence Maximization}},
author = {Huang, Keke and Wang, Sibo and Bevilacqua, Glenn and Xiao, Xiaokui and Lakshmanan, Laks V.S.},
journal = {PVLDB},
series = {{VLDB} '17},
volume = {10},
number = {9},
pages = {913--924},
doi = {10.14778/3099622.3099628},
url = {https://doi.org/10.14778/3099622.3099628},
year = {2017}
}Incoming Citations (Sorted by Pagerank)
Showing 21 of 21 citing papers.
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 194 | Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency | 2014 | SIGMOD | 0.000259047 |
| 296 | A Data-Based Approach to Social Influence Maximization | 2012 | VLDB | 0.00022220351 |
| 315 | Influence Maximization in Near-Linear Time: A Martingale Approach | 2015 | SIGMOD | 0.00021436156 |
| 453 | Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks | 2016 | SIGMOD | 0.00018145895 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 296 | A Data-Based Approach to Social Influence Maximization | 2012 | VLDB |
| 2 | 8,913 | Efficient Approximation Algorithms for Minimum Cost Seed Selection with Probabilistic Coverage Guarantee | 2024 | SIGMOD |
| 3 | 315 | Influence Maximization in Near-Linear Time: A Martingale Approach | 2015 | SIGMOD |
| 4 | 4,725 | Efficient Location-Aware Influence Maximization | 2014 | SIGMOD |
| 5 | 1,919 | Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models | 2016 | SIGMOD |
| 6 | 1,478 | Online Processing Algorithms for Influence Maximization | 2018 | SIGMOD |
| 7 | 2,260 | Online Topic-Aware Influence Maximization | 2015 | VLDB |
| 8 | 3,989 | Real-Time Influence Maximization on Dynamic Social Streams | 2017 | VLDB |
| 9 | 194 | Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency | 2014 | SIGMOD |
| 10 | 453 | Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks | 2016 | SIGMOD |