Related papers: Strategic Classification with Graph Neural Networks

Strategic Classification with Graph Neural Networks

URL: http://arxiv.org/abs/2205.15765v3
Date: Mon, 1 May 2023 13:57:37 GMT
Title: Strategic Classification with Graph Neural Networks
Authors: Itay Eilat, Ben Finkelshtein, Chaim Baskin, Nir Rosenfeld
Abstract summary: Using a graph for learning introduces inter-user dependencies in prediction. We propose a differentiable framework for strategically-robust learning of graph-based classifiers.
Score: 10.131895986034316
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: 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.

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