Research map / Learning theory and algorithms / Submodular and game algorithms
Fixation Maximization in the Positional Moran Process
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Lazy Greedy instead of the other evaluated heuristics | FM∞ · SNAP connected subgraphs, budgets 10% and 30% | normalized fixation probability | Helped |
| Use Vertex Cover instead of Lazy Greedy | FM∞ · SNAP connected subgraphs, budget 50% | normalized fixation probability | No clear effect |
| Use Weak Selector rather than the other evaluated heuristics | FM⁰ · SNAP connected subgraphs | normalized fixation probability derivative | Helped |
| Use Centrality rather than the other non-optimal heuristics | FM⁰ · SNAP connected subgraphs, especially larger budgets | normalized fixation probability derivative | Helped |
| Use Random node selection | FM∞ and FM⁰ · SNAP connected subgraphs | normalized fixation probability | Hurt |
| Use Weak Selector, optimized for weak selection, under strong selection | FM∞ · SNAP connected subgraphs | normalized fixation probability | Hurt |
| Choose the top-k nodes by the weak-selection derivative instead of graph-based heuristics | FM⁰ · Any graph | fixation probability derivative at δ=0 | Helped |
| Use lazy greedy under strong selection, where the objective is submodular | FM∞ · Undirected graphs | fixation probability: 1-1/e | Helped |
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