Efficient Algorithm for Budgeted Adaptive Influence Maximization: An Incremental RR-set Update Approach
Summary: Adaptive BIM with cost-aware greedy or a single influential node achieves an expected approximation under budget. Incremental RR-set updates keep extra info to fix RR-sets, enabling reuse and scalable diffusion; experiments show better influence and faster runtime. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Qintian Guo (Chinese University of Hong Kong)
- 2. Chen Feng (Chinese University of Hong Kong)
- 3. Fangyuan Zhang (Chinese University of Hong Kong)
- 4. Sibo Wang (Chinese University of Hong Kong)
BibTeX Citation
@inproceedings{guo_sigmod23,
title = {{Efficient Algorithm for Budgeted Adaptive Influence Maximization: An Incremental RR-set Update Approach}},
author = {Guo, Qintian and Feng, Chen and Zhang, Fangyuan and Wang, Sibo},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3617328},
url = {https://dl.acm.org/doi/10.1145/3617328},
year = {2023}
}
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,076 | Efficient Approximation Algorithms for Minimum Cost Seed Selection with Probabilistic Coverage Guarantee | 2024 | SIGMOD | 5.2283159e-05 |
| 10,871 | Efficient GPU-Accelerated Adaptive Minimum Cost Seed Selection | 2026 | VLDB | 4.9793485e-05 |
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