Revisiting Robustness in Graph Machine Learning

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Revisiting Robustness in Graph Machine Learning
Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann
International Conference on Learning Representations (ICLR), 2023


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Many works show that node-level predictions of Graph Neural Networks (GNNs) are unrobust to small, often termed adversarial, changes to the graph structure. However, because manual inspection of a graph is difficult, it is unclear if the studied perturbations always preserve a core assumption of adversarial examples: that of unchanged semantic content. To address this problem, we introduce a more principled notion of an adversarial graph, which is aware of semantic content change. Using Contextual Stochastic Block Models (CSBMs) and real-world graphs, our results uncover: i) for a majority of nodes the prevalent perturbation models include a large fraction of perturbed graphs violating the unchanged semantics assumption; ii) surprisingly, all assessed GNNs show over-robustness - that is robustness beyond the point of semantic change. We find this to be a complementary phenomenon to adversarial robustness related to the small degree of nodes and their class membership dependence on the neighbourhood structure.


You can cite our paper as follows:

title = {Revisiting Robustness in Graph Machine Learning},
author = {Gosch, Lukas and Sturm, Daniel and Geisler, Simon and G{\"u}nnemann, Stephan},
booktitle={International Conference on Learning Representations (ICLR)},
year = {2023}