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In-Person Poster presentation / poster accept

Revisiting Robustness in Graph Machine Learning

Lukas Gosch · Daniel Sturm · Simon Geisler · Stephan G√ľnnemann

MH1-2-3-4 #156

Keywords: [ adversarial robustness ] [ robustness ] [ label propagation ] [ non-i.i.d. data ] [ Bayes classifier ] [ node-classification ] [ graphs ] [ stochastic block models ] [ graph neural networks ] [ graph learning ] [ Social Aspects of Machine Learning ]


Abstract: 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 suggest: $i)$ 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 examples and show that including the label-structure of the training graph into the inference process of GNNs significantly reduces over-robustness, while having a positive effect on test accuracy and adversarial robustness. Theoretically, leveraging our new semantics-aware notion of robustness, we prove that there is no robustness-accuracy tradeoff for inductively classifying a newly added node.

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