Efficient Approximation Algorithms for Adaptive Seed Minimization
Summary: Adaptive seed minimization in social networks with batchwise feedback; ASTI exploits observed diffusion in rounds to reduce seeds. Offers (1 - (1 - 1/b)^b)(1 - 1/e)(1 - ε) approximation in expectation in O((η(m+n))/ε^2 ln n) time; first scalable adaptive guarantee, supported by experiments. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jing Tang (National University of Singapore)
- 2. Keke Huang (Nanyang Technological University)
- 3. Xiaokui Xiao (National University of Singapore)
- 4. Laks V.S. Lakshmanan (University of British Columbia)
- 5. Xueyan Tang (Nanyang Technological University)
- 6. Aixin Sun (Nanyang Technological University)
- 7. Andrew Lim (National University of Singapore)
BibTeX Citation
@inproceedings{tang_sigmod19,
title = {{Efficient Approximation Algorithms for Adaptive Seed Minimization}},
author = {Tang, Jing and Huang, Keke and Xiao, Xiaokui and Lakshmanan, Laks V.S. and Tang, Xueyan and Sun, Aixin and Lim, Andrew},
series = {{SIGMOD} '19},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3299869.3319881},
url = {https://dl.acm.org/doi/10.1145/3299869.3319881},
year = {2019}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 9 of 9 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 199 | Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency | 2014 | SIGMOD | 0.00025522558 |
| 299 | A Data-Based Approach to Social Influence Maximization | 2012 | VLDB | 0.00021812174 |
| 320 | Influence Maximization in Near-Linear Time: A Martingale Approach | 2015 | SIGMOD | 0.00021143147 |
| 455 | Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks | 2016 | SIGMOD | 0.0001796092 |
| 1,474 | Online Processing Algorithms for Influence Maximization | 2018 | SIGMOD | 0.00010556999 |
| 1,553 | Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study | 2017 | SIGMOD | 0.0001027493 |
| 1,964 | Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models | 2016 | SIGMOD | 9.3059474e-05 |
| 3,554 | Revisiting the Stop-and-Stare Algorithms for Influence Maximization | 2017 | VLDB | 7.2088217e-05 |
| 4,652 | Efficient Algorithms for Adaptive Influence Maximization | 2018 | VLDB | 6.485164e-05 |
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