Ising-Based Louvain Method: Clustering Large Graphs with Specialized Hardware

TL;DR

This paper introduces an Ising-based extension of the Louvain method, leveraging specialized hardware to improve large graph clustering performance, demonstrating significant improvements over existing algorithms.

Contribution

It develops a hybrid Ising-based Louvain method that enhances community detection on large graphs by utilizing specialized hardware, building on existing heuristics.

Findings

Outperforms two state-of-the-art community detection algorithms.

Effective on small to large-scale graphs.

Shows potential for adapting to other unsupervised learning heuristics.

Abstract

Recent advances in specialized hardware for solving optimization problems such quantum computers, quantum annealers, and CMOS annealers give rise to new ways for solving real-word complex problems. However, given current and near-term hardware limitations, the number of variables required to express a large real-world problem easily exceeds the hardware capabilities, thus hybrid methods are usually developed in order to utilize the hardware. In this work, we advocate for the development of hybrid methods that are built on top of the frameworks of existing state-of-art heuristics, thereby improving these methods. We demonstrate this by building on the so called Louvain method, which is one of the most popular algorithms for the Community detection problem and develop and Ising-based Louvain method. The proposed method outperforms two state-of-the-art community detection algorithms in clustering several small to large-scale graphs. The results show promise in adapting the same optimization approach to other unsupervised learning heuristics to improve their performance.