Continuous Influence Maximization: What Discounts Should We Offer to Social Network Users?
Summary: Continuous influence maximization: optimize per-user discounts under a budget to maximize expected adoption. Model-agnostic, coordinate-descent optimization; IC-specific efficiencies; four-network study shows gains with modest overhead over classical IM. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yu Yang
- 2. Xiangbo Mao
- 3. Jian Pei
- 4. Xiaofei He
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,165 | An In-Depth Comparison of s-t Reliability Algorithms over Uncertain Graphs | 2019 | VLDB | 5.1731827e-05 |
| 8,007 | Analysis of Influence Contribution in Social Advertising | 2022 | VLDB | 4.604154e-05 |
| 10,325 | Augmenting Social Influence of Uncertain Seeds via Probabilistic Link Insertion | 2026 | VLDB | 4.1905499e-05 |
| 11,142 | Host Profit Maximization: Leveraging Performance Incentives and User Flexibility | 2024 | VLDB | 4.1905499e-05 |
| 11,210 | Efficient Algorithm for Budgeted Adaptive Influence Maximization: An Incremental RR-set Update Approach | 2023 | SIGMOD | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 180 | Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency | 2014 | SIGMOD | 0.00037101192 |
| 338 | Influence Maximization in Near-Linear Time: A Martingale Approach | 2015 | SIGMOD | 0.00026987013 |
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