Label propagation for clustering

TL;DR

Label propagation is a simple, fast, and scalable heuristic for network clustering, suitable for large-scale community detection and graph partitioning, despite not being the most accurate method.

Contribution

This paper reviews the basic framework, advances, and extensions of label propagation, highlighting its efficiency and applicability to large networks and various clustering tasks.

Findings

Effective for large-scale community detection

Fast implementation on large networks

Versatile for different network structures

Abstract

Label propagation is a heuristic method initially proposed for community detection in networks, while the method can be adopted also for other types of network clustering and partitioning. Among all the approaches and techniques described in this book, label propagation is neither the most accurate nor the most robust method. It is, however, without doubt one of the simplest and fastest clustering methods. Label propagation can be implemented with a few lines of programming code and applied to networks with hundreds of millions of nodes and edges on a standard computer, which is true only for a handful of other methods in the literature. In this chapter, we present the basic framework of label propagation, review different advances and extensions of the original method, and highlight its equivalences with other approaches. We show how label propagation can be used effectively for large-scale community detection, graph partitioning, identification of structurally equivalent nodes and other network structures. We conclude the chapter with a summary of the label propagation methods and suggestions for future research.