Research map / Learning theory and algorithms / Submodular and game algorithms
Maximizing Activity in Ising Networks via the TAP Approximation
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use TAP or TAP3 rather than naive mean field for the continuous PGA gradient (ablation) | PGA for Ising influence maximization · Continuous influence optimization across the tested networks | total activity relative to optimal: within 20% of optimal (except for the arXiv network) | Helped |
| Use TAP or TAP3 gradient approximations instead of Monte Carlo-estimated gradients | PGA on a network of size 200 · Continuous influence optimization | runtime: ~10 minutes versus ~ 5 seconds | Helped |
| Use TAP3 instead of TAP or MF in the discrete greedy algorithm (ablation) | GI · Discrete influence, Erdős-Rényi networks | total activity: slightly outperforms TAP and MF | Helped |
| Use TAP, TAP3, or MF rather than linear-threshold influence maximization | Discrete Ising influence maximization · Discrete influence, Erdős-Rényi networks | total activity | Helped |
| Use Ising-based greedy selection instead of degree-based selection | Discrete Ising influence maximization · Discrete influence, Erdős-Rényi and arXiv networks | total activity | Helped |
| Select nodes using Ising-based greedy algorithms rather than LT greedy IM | Influence maximization evaluated under the linear threshold model · Erdős-Rényi networks | spread | Helped |
| Use TAP3 node selection rather than LT greedy IM | Influence maximization evaluated under the linear threshold model · Erdős-Rényi networks | spread | Helped |
| Use Ising-based greedy selection rather than LT greedy IM | Influence maximization evaluated under the linear threshold model · arXiv collaboration network | spread | Helped |
| Use LT greedy IM rather than the degree heuristic | Influence maximization evaluated under the linear threshold model · arXiv collaboration network | spread | Helped |
Closest papers by meaning
- Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution (NeurIPS 2016 · similarity 0.93)
- Concentration of Multilinear Functions of the Ising Model with Applications to Network Data (NeurIPS 2017 · similarity 0.87)
- Hyperparametric Robust and Dynamic Influence Maximization (AAAI 2025 · similarity 0.85)
- 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)
- DQSSA: A Quantum-Inspired Solution for Maximizing Influence in Online Social Networks (Student Abstract) (AAAI 2024 · similarity 0.84)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.