Research map / Learning theory and algorithms / Submodular and game algorithms
Influence Maximization with $\varepsilon$-Almost Submodular Threshold Functions
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use TIM+ (RRset) greedy on submodular lower/upper bound thresholds (Galg-L/Galg-U) instead of baselines | influence maximization on eps-AS graphs · NetHEPT, Flixster, DBLP | influence spread vs baselines: TIM-U/TIM-L outperform PageRank and High-degree in all cases | Helped |
| Use TIM-U/TIM-L instead of PageRank on Flixster with more eps-AS nodes and larger eps | influence maximization on eps-AS graphs · Flixster, eps=0.4 | influence spread vs PageRank: up to 74.2% better | Helped |
| Use TIM-U/TIM-L on DBLP with many eps-AS nodes | influence maximization on eps-AS graphs · DBLP, eps=0.2 | influence spread vs PageRank/High-degree: PageRank and High-degree only about 2.6% behind | No clear effect |
| Approximation-guaranteed algorithm on graphs with more eps-AS nodes (10000 vs 3000 eps-AS-1 nodes) (ablation) | TIM-U/TIM-L on NetHEPT · NetHEPT, eps=0.2 | influence size: influence drops obviously with 10000 nodes vs 3000 | Hurt |
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