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
- 2. Arijit Khan
- 3. Gao Cong
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,158 | The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches | 2020 | SIGMOD | 5.1800945e-05 |
| 11,072 | A Benchmark Study of Deep-RL Methods for Maximum Coverage Problems over Graphs | 2024 | VLDB | 4.1945683e-05 |
| 11,139 | Host Profit Maximization: Leveraging Performance Incentives and User Flexibility | 2024 | VLDB | 4.1945683e-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 |
|---|---|---|---|---|
| 90 | A Data-Based Approach to Social Influence Maximization | 2012 | VLDB | 0.00052068982 |
| 180 | Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency | 2014 | SIGMOD | 0.00037135181 |
| 337 | Influence Maximization in Near-Linear Time: A Martingale Approach | 2015 | SIGMOD | 0.00027011645 |
| 1,162 | k-Nearest Neighbors in Uncertain Graphs | 2010 | VLDB | 0.0001358105 |
| 1,850 | Real-time Targeted Influence Maximization for Online Advertisements | 2015 | VLDB | 0.00010328335 |
| 2,490 | Online Topic-Aware Influence Maximization | 2015 | VLDB | 8.6584707e-05 |
| 5,359 | Discovering Your Selling Points: Personalized Social Influential Tags Exploration | 2017 | SIGMOD | 5.5485493e-05 |
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