Dynamic Influence Analysis in Evolving Networks
Summary: First real-time fully-dynamic index for influence analysis on evolving networks; redesign of the Borgs et al. sketch with efficient update algorithms. Adds reachability-tree and skipping, space-efficient RNGs; demonstrates millisecond influence estimates and ~10× faster influence maximization with provable non-degeneracy after arbitrary updates. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Naoto Ohsaka
- 2. Takuya Akiba
- 3. Yuichi Yoshida
- 4. Ken-ichi Kawarabayashi
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,371 | Real-Time Influence Maximization on Dynamic Social Streams | 2017 | VLDB | 6.2459569e-05 |
| 6,158 | The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches | 2020 | SIGMOD | 5.1800945e-05 |
| 7,904 | Coarsening Massive Influence Networks for Scalable Diffusion Analysis | 2017 | SIGMOD | 4.6214923e-05 |
| 11,005 | Influence Maximization via Vertex Countering | 2024 | VLDB | 4.1945683e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 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 |
Previous
Page 1 / 1
Next