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 |
|---|---|---|---|---|
| 7,913 | Triangular Stability Maximization by Influence Spread over Social Networks | 2023 | VLDB | 5.4277727e-05 |
| 9,963 | Fast and Space-Efficient Parallel Algorithms for Influence Maximization | 2024 | VLDB | 5.1038322e-05 |
| 11,554 | Influence Maximization via Vertex Countering | 2024 | VLDB | 4.9793485e-05 |
| 11,663 | Host Profit Maximization: Leveraging Performance Incentives and User Flexibility | 2024 | VLDB | 4.9793485e-05 |
Previous
Page 1 / 1
Next
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 |
|---|---|---|---|---|
| 199 | Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency | 2014 | SIGMOD | 0.00025522558 |
| 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 |
| 1,553 | 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 |
| 2,229 | Real-time Targeted Influence Maximization for Online Advertisements | 2015 | VLDB | 8.8000047e-05 |
| 3,554 | Revisiting the Stop-and-Stare Algorithms for Influence Maximization | 2017 | VLDB | 7.2088217e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 8,958 | Finding Seeds and Relevant Tags Jointly: For Targeted Influence Maximization in Social Networks | 2018 | SIGMOD |
| 2 | 7,445 | Analysis of Influence Contribution in Social Advertising | 2022 | VLDB |
| 3 | 6,506 | Influence Maximization in Real-World Closed Social Networks | 2023 | VLDB |
| 4 | 299 | A Data-Based Approach to Social Influence Maximization | 2012 | VLDB |
| 5 | 5,823 | Continuous Influence Maximization: What Discounts Should We Offer to Social Network Users? | 2016 | SIGMOD |
| 6 | 4,652 | Efficient Algorithms for Adaptive Influence Maximization | 2018 | VLDB |
| 7 | 5,383 | GetReal: Towards Realistic Selection of Influence Maximization Strategies in Competitive Networks | 2015 | SIGMOD |
| 8 | 2,587 | From Competition to Complementarity: Comparative Influence Diffusion and Maximization | 2016 | VLDB |
| 9 | 5,980 | Maximizing Welfare in Social Networks under A Utility Driven Influence Diffusion model | 2019 | SIGMOD |
| 10 | 8,757 | Maximizing Social Welfare in a Competitive Diffusion Model | 2021 | VLDB |