Two provably consistent divide and conquer clustering algorithms for large networks

TL;DR

This paper introduces two divide-and-conquer clustering algorithms for large networks that significantly reduce computational costs while maintaining or improving accuracy, and are inherently parallelizable.

Contribution

The paper presents two novel divide-and-conquer algorithms for community detection that are provably consistent and scalable to large networks, outperforming traditional methods.

Findings

Algorithms are computationally efficient and scalable.

Maintain or improve clustering accuracy.

Validated through extensive simulations and real-data analysis.

Abstract

In this article, we advance divide-and-conquer strategies for solving the community detection problem in networks. We propose two algorithms which perform clustering on a number of small subgraphs and finally patches the results into a single clustering. The main advantage of these algorithms is that they bring down significantly the computational cost of traditional algorithms, including spectral clustering, semi-definite programs, modularity based methods, likelihood based methods etc., without losing on accuracy and even improving accuracy at times. These algorithms are also, by nature, parallelizable. Thus, exploiting the facts that most traditional algorithms are accurate and the corresponding optimization problems are much simpler in small problems, our divide-and-conquer methods provide an omnibus recipe for scaling traditional algorithms up to large networks. We prove consistency of these algorithms under various subgraph selection procedures and perform extensive simulations and real-data analysis to understand the advantages of the divide-and-conquer approach in various settings.