23.09.2021
14:00 - 15:00

 

Learning on graphs with logic and neural networks
Martin Ritzert
https://rwth.zoom.us/j/95250295111?pwd=cjhFMGk0bStZcGNvcmYrYURPWnQ0dz09 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.