Ricochet: A Family of Unconstrained Algorithms for Graph Clustering

Abstract

Partitional graph clustering algorithms like K-means and Star necessitate a priori decisions on the number of clusters and threshold for the weight of edges to be considered, respectively. These decisions are difficult to make and their impact on clustering performance is significant. We propose a family of algorithms for weighted graph clustering that neither requires a predefined number of clusters, unlike K-means, nor a threshold for the weight of edges, unlike Star. To do so, we use re-assignment of vertices as a halting criterion, as in K-means, and a metric for selecting clusters’ seeds, as in Star. Pictorially, the algorithms’ strategy resembles the rippling of stones thrown in a pond, thus the name ’Ricochet’. We evaluate the performance of our proposed algorithms using standard datasets and evaluate the impact of removing constraints by comparing the performance of our algorithms with constrained algorithms: K-means and Star and unconstrained algorithm: Markov clustering.