Talk is also available on Zoom
Graph Similarity: Concepts, Methods, and Challenges
Graph similarity plays a central role in network analysis, pattern recognition, and algorithmic data science. This talk provides a unified perspective on key concepts, methodological approaches, and computational challenges inherent in comparing complex graph structures.
We will discuss structural approaches, covering both distance-based techniques (e.g., graph edit distance) and isomorphism-based methods via subgraph search, alongside embedding-based techniques derived from the Weisfeiler-Leman refinement. A central theme of the talk is how algorithm engineering can bridge the gap between theoretical difficulty and practical applicability, enabling similarity computations on large and complex graph data.
Bio: Petra Mutzel is professor of computational analytics at the University of Bonn, where she is also the scientific director of the High Performance Computing and Analytics Lab at the Digital Science Center. She received her Ph.D. in computer science at the University of Cologne in 1994, followed by a postdoc position at the Max Planck Institute for Informatics in Saarbrücken and by professorships at the Vienna University of Technology from 2000 to 2004 and TU Dortmund University from 2004 to 2019.
Her research focuses on algorithm engineering, algorithmic data analysis, and combinatorial optimization for graphs. Currently, the main application areas are in geodesy, cheminformatics, and brain networks. She currently serves, or has previously served, on the Steering Committees of ESA, ALENEX, and WALCOM, as well as on the Editorial Boards of the ACM Journal on Experimental Algorithmics, Journal of Graph Algorithms and Applications (JGAA), and Mathematical Programming Computation (MPC). She is also a member of the supervisory board of CISPA, the advisory board of the Fraunhofer SCAI, and the scientific advisory board of FRIAS.