Finding Seeds and Relevant Tags Jointly: For Targeted Influence Maximization in Social Networks
Summary: Jointly selects top-k seeds and top-r tags to maximize influence in a target user set, modeling content-dependent diffusion (e.g., hashtags). To cope with intractability, it delivers heuristics with indexing and optimization for scalable performance. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiangyu Ke (Nanyang Technological University)
- 2. Arijit Khan (Nanyang Technological University)
- 3. Gao Cong (Nanyang Technological University)
BibTeX Citation
@inproceedings{ke_sigmod18,
title = {{Finding Seeds and Relevant Tags Jointly: For Targeted Influence Maximization in Social Networks}},
author = {Ke, Xiangyu and Khan, Arijit and Cong, Gao},
series = {{SIGMOD} '18},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3183713.3199670},
url = {https://dl.acm.org/doi/10.1145/3183713.3199670},
year = {2018}
}Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,872 | The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches | 2020 | SIGMOD | 5.9610813e-05 |
| 11,610 | A Benchmark Study of Deep-RL Methods for Maximum Coverage Problems over Graphs | 2024 | VLDB | 4.9769913e-05 |
| 11,669 | Host Profit Maximization: Leveraging Performance Incentives and User Flexibility | 2024 | VLDB | 4.9769913e-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 | 0.00025510476 |
| 299 | A Data-Based Approach to Social Influence Maximization | 2012 | VLDB | 0.00021801848 |
| 320 | Influence Maximization in Near-Linear Time: A Martingale Approach | 2015 | SIGMOD | 0.00021133139 |
| 1,064 | k-Nearest Neighbors in Uncertain Graphs | 2010 | VLDB | 0.00012202916 |
| 2,232 | Real-time Targeted Influence Maximization for Online Advertisements | 2015 | VLDB | 8.7958389e-05 |
| 2,309 | Online Topic-Aware Influence Maximization | 2015 | VLDB | 8.6639186e-05 |
| 5,304 | Discovering Your Selling Points: Personalized Social Influential Tags Exploration | 2017 | SIGMOD | 6.1867379e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 9,960 | Efficient and Effective Algorithms for A Family of Influence Maximization Problems with A Matroid Constraint | 2025 | VLDB |
| 2 | 2,280 | Influence Maximization Revisited: Efficient Reverse Reachable Set Generation with Bound Tightened | 2020 | SIGMOD |
| 3 | 4,655 | Efficient Algorithms for Adaptive Influence Maximization | 2018 | VLDB |
| 4 | 5,304 | Discovering Your Selling Points: Personalized Social Influential Tags Exploration | 2017 | SIGMOD |
| 5 | 2,698 | Efficient Algorithms for Budgeted Influence Maximization on Massive Social Networks | 2020 | VLDB |
| 6 | 299 | A Data-Based Approach to Social Influence Maximization | 2012 | VLDB |
| 7 | 2,309 | Online Topic-Aware Influence Maximization | 2015 | VLDB |
| 8 | 6,508 | Influence Maximization in Real-World Closed Social Networks | 2023 | VLDB |
| 9 | 2,232 | Real-time Targeted Influence Maximization for Online Advertisements | 2015 | VLDB |
| 10 | 4,055 | Real-Time Influence Maximization on Dynamic Social Streams | 2017 | VLDB |