The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches
Summary: Implementation-agnostic study of Oneshot, Snapshot, RIS analyzes random-solution distributions and the impact of sample size on quality. With enough samples, solutions converge to a unique optimum; Oneshot for limited memory, RIS for large networks, Snapshot for small networks. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Naoto Ohsaka
Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,003 | Analysis of Influence Contribution in Social Advertising | 2022 | VLDB | 4.6085729e-05 |
| 8,807 | Efficient and Effective Algorithms for Revenue Maximization in Social Advertising | 2021 | SIGMOD | 4.4455759e-05 |
| 9,098 | Scapin: Scalable Graph Structure Perturbation by Augmented Influence Maximization | 2023 | SIGMOD | 4.3967784e-05 |
| 11,005 | Influence Maximization via Vertex Countering | 2024 | VLDB | 4.1945683e-05 |
| 11,080 | Fast and Space-Efficient Parallel Algorithms for Influence Maximization | 2024 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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