Collective Influence Maximization for Multiple Competing Products with an Awareness-to-Influence Model
Summary: Two-phase Awareness-to-Influence (AtI) IM for multiple competing products: awareness propagates first, then users adopt the most preferred product. GCW treats products as agents in a monotone utility game, yielding efficient best-response with guarantees and scalable evaluation on large social networks. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Dimitris Tsaras (Hong Kong University of Science and Technology)
- 2. George Trimponias (Amazon)
- 3. Lefteris Ntaflos (Hong Kong University of Science and Technology)
- 4. Dimitris Papadias (Hong Kong University of Science and Technology)
BibTeX Citation
@article{tsaras_vldb21,
title = {{Collective Influence Maximization for Multiple Competing Products with an Awareness-to-Influence Model}},
author = {Tsaras, Dimitris and Trimponias, George and Ntaflos, Lefteris and Papadias, Dimitris},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {7},
pages = {1124--1136},
doi = {10.14778/3450980.3450981},
url = {https://doi.org/10.14778/3450980.3450981},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,771 | Triangular Stability Maximization by Influence Spread over Social Networks | 2023 | VLDB | 5.5470467e-05 |
| 11,216 | Influence Maximization via Vertex Countering | 2024 | VLDB | 5.093636e-05 |
| 11,286 | Fast and Space-Efficient Parallel Algorithms for Influence Maximization | 2024 | VLDB | 5.093636e-05 |
| 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 7 of 7 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 |
| 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 |
| 1,531 | Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study | 2017 | SIGMOD | 0.00010472526 |
| 1,919 | Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models | 2016 | SIGMOD | 9.4850778e-05 |
| 2,199 | Real-time Targeted Influence Maximization for Online Advertisements | 2015 | VLDB | 8.9668012e-05 |
| 3,639 | Revisiting the Stop-and-Stare Algorithms for Influence Maximization | 2017 | VLDB | 7.2335041e-05 |
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