Advanced Graph Clustering Methods: A Comprehensive and In-Depth Analysis

TL;DR

This paper provides a comprehensive review of traditional and modern graph clustering techniques, including deep learning approaches, with experimental comparisons and discussions on applications and future directions.

Contribution

It offers an in-depth analysis and comparison of classical and deep learning-based graph clustering methods, highlighting recent advancements and practical implications.

Findings

Deep learning methods outperform traditional algorithms in certain scenarios.

Spectral clustering remains a foundational technique with broad applicability.

The paper identifies promising future research directions in graph clustering.

Abstract

Graph clustering, which aims to divide a graph into several homogeneous groups, is a critical area of study with applications that span various fields such as social network analysis, bioinformatics, and image segmentation. This paper explores both traditional and more recent approaches to graph clustering. Firstly, key concepts and definitions in graph theory are introduced. The background section covers essential topics, including graph Laplacians and the integration of Deep Learning in graph analysis. The paper then delves into traditional clustering methods, including Spectral Clustering and the Leiden algorithm. Following this, state-of-the-art clustering techniques that leverage deep learning are examined. A comprehensive comparison of these methods is made through experiments. The paper concludes with a discussion of the practical applications of graph clustering and potential future research directions.