Strategic classification studies learning in settings where users can modify their features to obtain favorable predictions. Most current works focus on simple classifiers that trigger independent user responses. Here we examine the implications of learning with more elaborate models that break the independence assumption. Motivated by the idea that applications of strategic classification are often social in nature, we focus on \emph{graph neural networks}, which make use of social relations between users to improve predictions. Using a graph for learning introduces inter-user dependencies in prediction; our key point is that strategic users can exploit these to promote their goals. As we show through analysis and simulation, this can work either against the system -- or for it. Based on this, we propose a differentiable framework for strategically-robust learning of graph-based classifiers. Experiments on several real networked datasets demonstrate the utility of our approach.

翻译：策略分类研究的是用户可能通过修改自身特征以获取有利预测结果的学习场景。现有工作大多聚焦于触发独立用户响应的简单分类器。本文考察使用打破独立性假设的更复杂模型进行学习所带来的影响。基于策略分类应用通常具有社会性这一观点，我们重点关注利用用户间社会关系改进预测的**图神经网络**。使用图进行学习会在预测中引入用户间依赖关系；我们的核心观点是：策略性用户可利用这种依赖关系达成自身目标。通过分析与仿真，我们发现这种依赖关系既可能损害系统性能，也可能使系统受益。据此，我们提出一种面向图分类器的策略鲁棒学习可微框架。在多个真实网络数据集上的实验验证了该方法的有效性。