This page is about our paper

Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions

by Thomas Bonald, Bertrand Charpentier, Alexis Galland and Alexandre Hollocou
Published at the Mining and Learning with Graphs (MLG - KDD Workshop), 2018


We present a novel hierarchical graph clustering algorithm inspired by modularity-based clustering techniques. The algorithm is agglomerative and based on a simple distance between clusters induced by the probability of sampling node pairs. We prove that this distance is reducible, which enables the use of the nearest-neighbor chain to speed up the agglomeration. The output of the algorithm is a regular dendrogram, which reveals the multi-scale structure of the graph. The results are illustrated on both synthetic and real datasets. Links

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