Learning Graph Representations by Dendrograms

Thomas Bonald, Bertrand Charpentier

Abstract:

Hierarchical clustering is a common approach to analysing the
multi-scale structure of graphs observed in practice.
We propose a novel metric for assessing the quality of a hierarchical clustering. This metric reflects the ability to reconstruct the graph from the dendrogram encoding the hierarchy. The best representation of the graph for this metric in turn yields a novel hierarchical clustering algorithm. Experiments on both real and synthetic data illustrate the efficiency of the approach.

Keywords: Graph, hierarchical clustering, dendrogram, quality metric, reconstruction, entropy

TL;DR: Novel quality metric for hierarchical graph clustering