End-to-End Learning of Probabilistic Hierarchies on Graphs

Daniel Zügner, Bertrand Charpentier, Morgane Ayle, Sascha Geringer, Stephan Günnemann

Keywords: hierarchical clustering, graphs, networks, graph mining, network mining, graph custering

Abstract:

We propose a novel probabilistic model over hierarchies on graphs obtained by continuous relaxation of tree-based hierarchies. We draw connections to Markov chain theory, enabling us to perform hierarchical clustering by efficient end-to-end optimization 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.

One-sentence Summary: End-to-end gradient-based hierarchical clustering on graphs by exploiting Markov chain theory leads to state-of-the-art results.