This page is about our paper

End-to-End Learning of Probabilistic Hierarchies on Graphs

by Daniel Zügner, Bertrand Charpentier, Sascha Geringer, Morgane Ayle, and Stephan Günnemann
Published at the International Conference on Learning Representations (ICLR), 2022


We propose a novel probabilistic model over hierarchies on graphs obtained bycontinuous relaxation of tree-based hierarchies. We draw connections to Markov chain theory, enabling us to perform hierarchical clustering by efficient end-to-endoptimization of relaxed versions of quality metrics such as Dasgupta cost or Tree-Sampling Divergence (TSD). We show that our model learns rich, high-quality hierarchies present in 11 real world graphs, including a large graph with 2.3M nodes. Our model consistently outperforms recent as well as strong traditional baselines such as average linkage. Our model also obtains strong results on link prediction despite not being trained on this task, highlighting the quality of the hierarchies discovered by our model.


Paper Video Github