Unconstrained Submodular Maximization with Modular Costs: Tight Approximation and Application to Profit Maximization
Summary: Introduces ROI-Greedy for unconstrained monotone submodular maximization with modular costs, achieving a novel logarithmic additive guarantee that is provably tight. Extends it to sampling-based profit maximization, with strong empirical utility. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tianyuan Jin (National University of Singapore)
- 2. Yu Yang (City University of Hong Kong)
- 3. Renchi Yang (National University of Singapore)
- 4. Jieming Shi (Hong Kong Polytechnic University)
- 5. Keke Huang (National University of Singapore)
- 6. Xiaokui Xiao (National University of Singapore)
BibTeX Citation
@article{jin_vldb21,
title = {{Unconstrained Submodular Maximization with Modular Costs: Tight Approximation and Application to Profit Maximization}},
author = {Jin, Tianyuan and Yang, Yu and Yang, Renchi and Shi, Jieming and Huang, Keke and Xiao, Xiaokui},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {10},
pages = {1756--1768},
doi = {10.14778/3467861.3467866},
url = {https://doi.org/10.14778/3467861.3467866},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,345 | Host Profit Maximization: Leveraging Performance Incentives and User Flexibility | 2024 | VLDB | 5.093636e-05 |
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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 |
| 2,752 | Efficient Algorithms for Budgeted Influence Maximization on Massive Social Networks | 2020 | VLDB | 8.1648436e-05 |
| 3,639 | Revisiting the Stop-and-Stare Algorithms for Influence Maximization | 2017 | VLDB | 7.2335041e-05 |
| 5,920 | Efficient Approximation Algorithms for Adaptive Seed Minimization | 2019 | SIGMOD | 6.0418521e-05 |
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