Distributed Detection of Clusters of Arbitrary Size

Abstract

Graph clustering is a fundamental technique in data analysis with a vast number of applications in computer science and statistics. In theoretical computer science, the problem of graph clustering has received significant research attention over the past two decades, which has led to pivotal algorithmic breakthroughs. However, the design of most graph clustering algorithms is based on complicated techniques from computational optimisation, which are not applicable for processing massive data sets stored in physically remote locations.In this work we present a novel distributed algorithm for graph clustering. Most of the previous algorithms only work for graphs with balanced-sized clusters, which restrict their applications in many practical settings. Our proposed algorithm works for graphs with clusters of arbitrary size and its performance is analysed with respect to every individual cluster. In addition, our algorithm is easy to implement, and only requires a poly-logarithmic number of rounds for many graphs occurring in practice.KeywordsDistributed computingGraph clusteringRandomised algorithms