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
