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End-to-End Learning of Probabilistic Hierarchies on Graphs

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 quality metrics such as Dasgupta cost or Tree-Sampling Divergence (TSD). We show that our model learns rich, high-quality hierarchies present in 8 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. We demonstrate that our model also obtains competitive results on link prediction despite not being trained on this task, highlighting the quality of the hierarchies discovered by our model.

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