Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study
Summary: Benchmarks IM methods under identical conditions via a unified platform, enabling apples-to-apples comparisons. Finds deficiencies, debunks IM myths, and shows no universally dominant technique; performance depends on the metric. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Akhil Arora
- 2. Sainyam Galhotra
- 3. Sayan Ranu
Incoming Citations (Sorted by Pagerank)
Showing 17 of 17 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 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 |
| 436 | Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks | 2016 | SIGMOD | 0.00023259324 |
| 2,220 | Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models | 2016 | SIGMOD | 9.2622402e-05 |
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