Near linear time algorithm to detect community structures in large-scale networks

TL;DR

This paper introduces a simple, fast label propagation algorithm for detecting communities in large networks without prior information, achieving near-linear time complexity and effective results.

Contribution

The paper presents a novel, computationally efficient label propagation method that detects communities in large-scale networks without requiring pre-defined parameters or optimization.

Findings

Algorithm runs in near-linear time, making it suitable for large networks.

Effective in identifying community structures without prior information.

Validated on networks with known community structures.

Abstract

Community detection and analysis is an important methodology for understanding the organization of various real-world networks and has applications in problems as diverse as consensus formation in social communities or the identification of functional modules in biochemical networks. Currently used algorithms that identify the community structures in large-scale real-world networks require a priori information such as the number and sizes of communities or are computationally expensive. In this paper we investigate a simple label propagation algorithm that uses the network structure alone as its guide and requires neither optimization of a pre-defined objective function nor prior information about the communities. In our algorithm every node is initialized with a unique label and at every step each node adopts the label that most of its neighbors currently have. In this iterative process densely connected groups of nodes form a consensus on a unique label to form communities. We validate the algorithm by applying it to networks whose community structures are known. We also demonstrate that the algorithm takes an almost linear time and hence it is computationally less expensive than what was possible so far.