  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.
