Graduate Seminar: Martin Ritzert: Learning on Graphs with Logic and Neural Networks
Thursday, September 23, 2021, 2:00pm
Ort: Zoom-Videokonferenz und Raum 9222
For participation on-site a registration is required
Vortragender: Martin Ritzert
Learning on Graphs with Logic and Neural Networks
In the domain of graphs we show strong connections between logic and machine learning in both theory and practice. In a purely theoretical framework we develop sublinear machine learning algorithms for supervised learning of logical formulas on various graph classes. Further we show that learning first-order logic on arbitrary graphs is intractable unless P=NP. At the intersection of theory and practice, we prove an equivalence between graph neural networks and the 1-dimensional Weisfeiler-Leman algorithm. As a practical application, we approximate combinatorial problems with recurrent graph neural networks. The proposed architecture is unsupervised and can be applied to all maximum constraint satisfaction problems.