# StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks

arXiv: https://arxiv.org/abs/2009.14337 · HTML: https://mlsanity.com/paper/2009.14337/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2020.

## Built on by (most cited first, 5 of 5)

- [A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization](https://mlsanity.com/paper/2211.03074/) (2022)
- [USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization Problems](https://mlsanity.com/paper/2107.07508/) (2021)
- [Learnability of Competitive Threshold Models](https://mlsanity.com/paper/2205.03750/) (2022)
- [Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations](https://mlsanity.com/paper/2209.10493/) (2022)
- [Meta-GCN: A Dynamically Weighted Loss Minimization Method for Dealing with the Data Imbalance in Graph Neural Networks](https://mlsanity.com/paper/2406.17073/) (2024)

## Abstract (arXiv)

Given a combinatorial optimization problem taking an input, can we learn a strategy to solve it from the examples of input-solution pairs without knowing its objective function? In this paper, we consider such a setting and study the misinformation prevention problem. Given the examples of attacker-protector pairs, our goal is to learn a strategy to compute protectors against future attackers, without the need of knowing the underlying diffusion model. To this end, we design a structured prediction framework, where the main idea is to parameterize the scoring function using random features constructed through distance functions on randomly sampled subgraphs, which leads to a kernelized scoring function with weights learnable via the large margin method. Evidenced by experiments, our method can produce near-optimal protectors without using any information of the diffusion model, and it outperforms other possible graph-based and learning-based methods by an evident margin.