Keynote Talk
Verification of Graph Learning Models and Analysis of Query Languages
Graph neural networks (GNNs) are the predominant architectures for a variety of learning tasks on graphs. Like other modern machine learning models, their success is accompanied by many fundamental concerns. Can we understand what exactly a trained model does? Can we have confidence that a trained model will not do anything bad? In this talk we will give one of the first approaches to verifying GNNs. We will work using the static analysis of techniques of database theory. Many of our results work by converting the GNN into a logic, and then showing that we can analyze the logic. This translation is useful not only for verification, but for explanation of the behavior of GNNs.
The work reported here is joint with Chia-Hsuan Lu, Boris Motik, and Tony Tan (Decidability of Graph Neural Networks via Logical Characterizations), it is closely related to work of Barcelo, Kostylev, Monet, Reutter, and Silva from ICLR 2020 (The Logical Expressiveness of Graph Neural Networks), and also to prior work on analysis of logics on graphs with arithmetic (On two-variable guarded fragment logic with expressive local Presburger constraints; Two Variable Logic with Ultimately Periodic Counting). The talk will be in the same spirit as the AMW summer school course on analysis of GNNs via query languages: but there will be no dependency between the two, and no overlap in the results.
Bio
Michael Benedikt is a professor at Oxford University’s computer science department, and a fellow of University College Oxford. He came to Oxford after a decade in US industrial research laboratories, including a position as Distinguished Member of Technical Staff at Bell Laboratories. He has worked extensively in computational logic, finite model theory, verification, and data management, and specializes in the interaction between these topics. He has been a keynote in past meetings on mathematical logic, computational logic, description logics, and databases. He has co-authored papers receiving best paper awards and test-of-time awards in major conferences within databases and theoretical computer science, and he has served as the program chair of both the ACM Principles of Database Systems conference (2012) and the International Conference on Database Theory (2017). He currently holds an Established Career Fellowship from the UK’s Engineering and Physical Science’s Research Council, and serves on the steering committees for the Association for Symbolic Logic, the European Association for Theoretical Computer Science, and the International Conference on Database Theory.
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Maribel Acosta is the Professor of Data Engineering at the TUM Campus Heilbronn since August 2023. Maribel Acosta studied Computer Science at Universidad Simon Bolivar, Venezuela. From 2012 to 2017, she was a research assistant at the Karlsruhe Institute of Technology (KIT), where she received her doctorate. She then worked as a postdoc and deputy professor at KIT until 2020. Afterward, she was appointed as the professor for Databases and Information Systems at the Ruhr-University Bochum until July 2023. She is actively involved in the scientific communities on Data Management and Artificial Intelligence. Her work has received several “Best Paper Awards” and she serves as chair and reviewer for renowned conferences. Besides research, Maribel Acosta has many years of teaching experience in Databases, Big Data, and Knowledge Graphs and has received two “Best Teaching Awards”. Maribel Acosta investigates techniques for managing knowledge graphs. Her contributions include efficient solutions for querying knowledge graphs while providing high-quality answers.
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Software Engineer and Researcher at the IT Department of Banca d’Italia since 2010. My research interests include database theory, data models, schema and data translation, data integration and data exchange, artificial intelligence, in particular logic-based methods of AI, and knowledge graphs.
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