Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction

TL;DR

This paper introduces an advanced label propagation algorithm that combines defensive and offensive strategies to effectively detect communities and extract network cores in large complex networks, demonstrating superior accuracy and scalability.

Contribution

The paper presents a novel hierarchical label propagation algorithm that integrates defensive preservation and offensive expansion strategies for improved community detection.

Findings

Comparable to state-of-the-art algorithms in accuracy

Superior to previous label propagation methods

Almost linear complexity with respect to number of edges

Abstract

Label propagation has proven to be a fast method for detecting communities in large complex networks. Recent developments have also improved the accuracy of the approach, however, a general algorithm is still an open issue. We present an advanced label propagation algorithm that combines two unique strategies of community formation, namely, defensive preservation and offensive expansion of communities. Two strategies are combined in a hierarchical manner, to recursively extract the core of the network, and to identify whisker communities. The algorithm was evaluated on two classes of benchmark networks with planted partition and on almost 25 real-world networks ranging from networks with tens of nodes to networks with several tens of millions of edges. It is shown to be comparable to the current state-of-the-art community detection algorithms and superior to all previous label propagation algorithms, with comparable time complexity. In particular, analysis on real-world networks has proven that the algorithm has almost linear complexity, $\mathcal{O}(m^{1.19})$, and scales even better than basic label propagation algorithm ($m$ is the number of edges in the network).