论文标题
图形神经网络的战略分类
Strategic Classification with Graph Neural Networks
论文作者
论文摘要
战略分类研究在设置中学习，用户可以修改其功能以获得有利的预测。当前大多数工作都集中在触发独立用户响应的简单分类器上。在这里，我们通过更精细的模型来研究学习的含义，这些模型破坏了独立性假设。由战略分类的应用通常是社会性质的，我们专注于\ emph {图神经网络}的想法，这些想法利用用户之间的社会关系来改善预测。使用图进行学习会引入预测中的用户间依赖性；我们的关键是，战略用户可以利用这些用户来促进其目标。正如我们通过分析和仿真所显示的那样，这可以针对系统或为其作用。基于此，我们为基于图形的分类器进行战略性学习的框架提供了一个可区分的框架。在几个实际网络数据集上进行的实验证明了我们方法的实用性。
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.
