Hierarchical Overlapping Clustering of Network Data Using Cut Metrics

TL;DR

This paper introduces a new hierarchical overlapping clustering method for network data that combines ultrametrics and cut metrics, enabling nested and overlapping groupings, with applications demonstrated on real-world datasets.

Contribution

The paper presents a novel approach to derive hierarchical overlapping clusters from network data using convex combinations of ultrametrics and cut metrics, including directed networks.

Findings

Effective in synthetic and real-world classification tasks

Allows overlapping and hierarchical clustering simultaneously

Preserves asymmetry in directed networks

Abstract

A novel method to obtain hierarchical and overlapping clusters from network data -i.e., a set of nodes endowed with pairwise dissimilarities- is presented. The introduced method is hierarchical in the sense that it outputs a nested collection of groupings of the node set depending on the resolution or degree of similarity desired, and it is overlapping since it allows nodes to belong to more than one group. Our construction is rooted on the facts that a hierarchical (non-overlapping) clustering of a network can be equivalently represented by a finite ultrametric space and that a convex combination of ultrametrics results in a cut metric. By applying a hierarchical (non-overlapping) clustering method to multiple dithered versions of a given network and then convexly combining the resulting ultrametrics, we obtain a cut metric associated to the network of interest. We then show how to extract a hierarchical overlapping clustering structure from the aforementioned cut metric. Furthermore, the so-called overlapping function is presented as a tool for gaining insights about the data by identifying meaningful resolutions of the obtained hierarchical structure. Additionally, we explore hierarchical overlapping quasi-clustering methods that preserve the asymmetry of the data contained in directed networks. Finally, the presented method is illustrated via synthetic and real-world classification problems including handwritten digit classification and authorship attribution of famous plays.