REVISITING ROBUSTNESS IN GRAPH MACHINE LEARNING

Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan Günnemann

Research output: Contribution to conferencePaperpeer-review

8 Scopus citations

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 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 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.

Original languageEnglish
StatePublished - 2023
Event11th International Conference on Learning Representations, ICLR 2023 - Kigali, Rwanda
Duration: 1 May 20235 May 2023

Conference

Conference11th International Conference on Learning Representations, ICLR 2023
Country/TerritoryRwanda
CityKigali
Period1/05/235/05/23

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