论文标题
Stratlearner：在社交网络中学习预防错误信息的策略
StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks
论文作者
论文摘要
给定一个组合优化问题，我们可以学习一种策略以在不知道其目标函数的情况下从输入 - 解决方案对的示例中解决它吗？在本文中，我们考虑了这样的环境并研究了预防错误信息问题。鉴于攻击者预生长对的示例，我们的目标是学习一种策略来计算保护者免受未来攻击者的影响，而无需了解潜在的扩散模型。为此，我们设计了一个结构化的预测框架，其中主要思想是使用通过距离函数在随机采样子图上构建的随机特征来对评分函数进行参数化，从而通过大范围方法可以通过可学习的权重来实现kerneled评分函数。通过实验证明，我们的方法可以在不使用扩散模型的任何信息的情况下产生近乎最佳的保护膜，并且通过明显的边缘优于其他可能的基于图和学习的方法。
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.
