StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks

TL;DR

This paper introduces StratLearner, a novel learning-based approach for misinformation prevention in social networks that does not require knowledge of the diffusion model, achieving near-optimal protection strategies.

Contribution

It proposes a structured prediction framework using kernelized scoring functions learned via large margin methods to effectively prevent misinformation without diffusion model details.

Findings

Outperforms existing graph-based and learning-based methods

Produces near-optimal protectors without diffusion model information

Demonstrates effectiveness through experimental validation

Abstract

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.