Research map / Learning theory and algorithms / Submodular and game algorithms
Computing and maximizing influence in linear threshold and triggering models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use lower bound LB1 (single-edge) greedy instead of simulation-based greedy for influence maximization | linear threshold influence maximization · Erdös-Renyi, preferential attachment, 2D-grid graphs | runtime relative to LB1 greedy: LB1 1.00 vs simulation 710.58 (ER) | Helped |
| Use the tight bounds LB2 instead of LB1 in greedy selection | linear threshold influence maximization · Erdös-Renyi, preferential attachment, 2D-grid graphs | influence of selected set (simulated) | No clear effect |
| Bound-based greedy (LB1, LB2, UB) vs uniform random seed selection | influence maximization with |A|=10 · Erdös-Renyi, preferential attachment, 2D-grid graphs | influence of selected set | Helped |
| Small maximum outgoing weight of non-seed nodes (lambda small) closes gap between upper and lower bounds | linear threshold model bounds · Erdös-Renyi, preferential attachment, 2D-grid | gap between upper bound UB and LB1/LB2 | Helped |
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