Research map / Learning theory and algorithms / Submodular and game algorithms
Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Shift in MF-optimal field from high-degree to low-degree nodes as interaction strength increases | MF-IIM on hub-and-spoke network · hub-and-spoke network, H=1 | average degree of optimal field nodes | Helped |
| MF optimal external field versus exact optimal field | MF-IIM on hub-and-spoke network · hub-and-spoke network, H=1 | ratio of MF to exact magnetization | Mixed |
| MF optimal field versus degree-proportional, centrality-proportional, and random heuristics at beta_c | MF-IIM on stochastic block network · stochastic block network, 100 nodes, H=20 | magnetization (Monte Carlo) | Helped |
| MF optimal field versus node-selection heuristics at beta_c | MF-IIM on arXiv collaboration network · collaboration network, 904 nodes, H=40 | magnetization | Helped |
| Shift in MF-optimal field from high- to low-degree nodes near beta_c=0.05 | MF-IIM on arXiv collaboration network · collaboration network, 904 nodes, H=40 | node degree of selected nodes | Helped |
Closest papers by meaning
- Maximizing Activity in Ising Networks via the TAP Approximation (AAAI 2018 · similarity 0.93)
- Concentration of Multilinear Functions of the Ising Model with Applications to Network Data (NeurIPS 2017 · similarity 0.88)
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- Hyperparametric Robust and Dynamic Influence Maximization (AAAI 2025 · similarity 0.84)
- DiffIM: Differentiable Influence Minimization with Surrogate Modeling and Continuous Relaxation (AAAI 2025 · similarity 0.84)
- Unveiling Environmental Sensitivity of Individual Gains in Influence Maximization (NeurIPS 2025 · similarity 0.84)
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