Revisiting Robustness in Graph Machine Learning

• URL: http://arxiv.org/abs/2305.00851v2
• Date: Tue, 2 May 2023 08:12:34 GMT
• Title: Revisiting Robustness in Graph Machine Learning
• Authors: Lukas Gosch, Daniel Sturm, Simon Geisler, Stephan G\"unnemann
• Abstract summary: Many works show that node-level predictions of Graph Neural Networks (GNNs) are unrobust to small, often termed adversarial, changes to the graph structure. We introduce a more principled notion of an adversarial graph, which is aware of semantic content change. We find that including the label-structure of the training graph into the inference process of GNNs significantly reduces over-robustness.
• Score: 1.5293427903448025
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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