Learning with Structured Data by Matching and Embedding Graphs


Talk by Nils Kriege (Research Group Data Mining and Machine Learning)


Data mining and machine learning for structured data is becoming increasingly important in domains such as social network analysis, computer vision or chem- and bioinformatics. In this talk, I give an overview of my work in this area with a focus on applications in cheminformatics. The talk is divided into three closely connected parts.

The maximum common subgraph problem asks for a largest substructure that is contained in two given graphs. The problem is NP-hard in general. I introduce polynomial-time algorithms for trees and tree-like graphs. Motivated by constraints relevant in cheminformatics a variation of the problem is formalized and solved efficiently in series-parallel graphs.

Graph kernels are specific similarity measures for graphs, which enable the application of established machine learning approaches such as support vector machines to graphs. I will present kernels based on Weisfeiler-Lehman refinement.

Graph neural networks extend deep learning techniques, which have been proven to be extremely successful for data such as images, to directly operate on graphs. I will present a technique for deep graph matching, which learns and refines feature representations to reach a consensus mapping.


Where and when?

26.03.2021, 12:00


Zoom-Meeting-ID: 954 8495 6002

Password: dsunivie21