PageRank and The K-Means Clustering Algorithm

TL;DR

This paper introduces a novel clustering method that leverages PageRank and centrality measures to improve k-means clustering on graphs and extends its application to metric spaces like point clouds and meshes.

Contribution

It generalizes k-means clustering using PageRank for directed and undirected graphs and extends the approach to metric spaces, broadening its applicability.

Findings

PageRank-based clustering is effective for graph data.

The method is adaptable to metric spaces such as point clouds.

Centrality measures enhance clustering robustness.

Abstract

We utilize the PageRank vector to generalize the $k$-means clustering algorithm to directed and undirected graphs. We demonstrate that PageRank and other centrality measures can be used in our setting to robustly compute centrality of nodes in a given graph. Furthermore, we show how our method can be generalized to metric spaces and apply it to other domains such as point clouds and triangulated meshes