Community Detection in Dynamic Networks via Adaptive Label Propagation

TL;DR

The paper introduces ALPA, an adaptive label propagation algorithm that efficiently detects and tracks communities in dynamic networks by updating local labels based on historical data, reducing computational costs.

Contribution

It presents a novel, low-complexity, parameter-free method for dynamic community detection that outperforms existing approaches in accuracy and scalability.

Findings

ALPA effectively detects and monitors communities in synthetic and real-world networks.

ALPA achieves high accuracy and quality in community detection.

ALPA demonstrates low computational cost and scalability.

Abstract

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation process, which generally affects only a small portion of the network. This makes it respond to network changes at low computational cost. The effectiveness of ALPA has been tested on both synthetic and real-world networks, which shows that it can successfully identify and track dynamic communities. Moreover, ALPA could detect communities with high quality and accuracy compared to other methods. Therefore, being low-complexity and parameter-free, ALPA is a scalable and promising solution for some real-world applications of community detection in dynamic networks.