Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency
Summary: TIM bridges theory and practice for influence maximization under IC, LT, and triggering models. Near-optimal O((k+l)(n+m) log n / e^2) time with (1-1/e - e)-approx and practical heuristics; scales to very large graphs on commodity hardware, beating prior guaranteed methods by orders of magnitude. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Youze Tang
- 2. Xiaokui Xiao
- 3. Yanchen Shi
Incoming Citations (Sorted by Pagerank)
Showing 3 of 53 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,202 | Mitigating Filter Bubbles Under a Competitive Diffusion Model | 2023 | SIGMOD | 4.1945683e-05 |
| 11,208 | Efficient Algorithm for Budgeted Adaptive Influence Maximization: An Incremental RR-set Update Approach | 2023 | SIGMOD | 4.1945683e-05 |
| 11,537 | Towards an Efficient Weighted Random Walk Domination | 2021 | VLDB | 4.1945683e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 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 |
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