4 papers
Exact Verification of Graph Neural Networks with Incremental Constraint Solving
Minghao Liu, Chia-Hsuan Lu, Marta Kwiatkowska
Graph neural networks (GNNs) are increasingly often employed in high-stakes applications, such as fraud detection or healthcare, but are susceptible to adversarial attacks. A numbe…
Robustness Verification of Graph Neural Networks Via Lightweight Satisfiability Testing
Chia-Hsuan Lu, Tony Tan, Michael Benedikt
Graph neural networks (GNNs) are the predominant architecture for learning over graphs. As with any machine learning model, an important issue is the detection of attacks, where an…
Analysis of logics with arithmetic
Michael Benedikt, Chia-Hsuan Lu, Tony Tan
We present new results on finite satisfiability of logics with counting and arithmetic. One result is a tight bound on the complexity of satisfiability of logics with so-called loc…
Decidability of Graph Neural Networks via Logical Characterizations
Michael Benedikt, Chia-Hsuan Lu, Tony Tan
We present results concerning the expressiveness and decidability of a popular graph learning formalism, graph neural networks (GNNs), exploiting connections with logic. We use a f…