Local Algorithms for Finding Densely Connected Clusters

TL;DR

This paper introduces local algorithms designed to identify pairs of densely interconnected vertex sets in large graphs, emphasizing inter-cluster connections and providing a new reduction technique for analyzing multiple sets.

Contribution

It presents novel local algorithms for detecting densely connected cluster pairs based on inter-connection structure, along with a reduction method linking multiple sets to a single set in a reduced graph.

Findings

Successfully recovers densely connected clusters in real-world datasets

Introduces a new reduction technique for analyzing multiple sets

Enhances understanding of inter-cluster relationships in large graphs

Abstract

Local graph clustering is an important algorithmic technique for analysing massive graphs, and has been widely applied in many research fields of data science. While the objective of most (local) graph clustering algorithms is to find a vertex set of low conductance, there has been a sequence of recent studies that highlight the importance of the inter-connection between clusters when analysing real-world datasets. Following this line of research, in this work we study local algorithms for finding a pair of vertex sets defined with respect to their inter-connection and their relationship with the rest of the graph. The key to our analysis is a new reduction technique that relates the structure of multiple sets to a single vertex set in the reduced graph. Among many potential applications, we show that our algorithms successfully recover densely connected clusters in the Interstate Disputes Dataset and the US Migration Dataset.