Community Detection Using A Neighborhood Strength Driven Label Propagation Algorithm

TL;DR

This paper introduces an improved label propagation algorithm for community detection in large social networks, enhancing both speed and accuracy by refining update rules and considering neighborhood strength.

Contribution

It proposes a new update rule and label propagation criterion that significantly improve efficiency and community quality over the original LPA.

Findings

Reduced number of iterations by an order of magnitude for large networks

Improved community detection quality on both synthetic and real-world networks

Neighborhood strength consideration enhances detection in networks with high clustering

Abstract

Studies of community structure and evolution in large social networks require a fast and accurate algorithm for community detection. As the size of analyzed communities grows, complexity of the community detection algorithm needs to be kept close to linear. The Label Propagation Algorithm (LPA) has the benefits of nearly-linear running time and easy implementation, thus it forms a good basis for efficient community detection methods. In this paper, we propose new update rule and label propagation criterion in LPA to improve both its computational efficiency and the quality of communities that it detects. The speed is optimized by avoiding unnecessary updates performed by the original algorithm. This change reduces significantly (by order of magnitude for large networks) the number of iterations that the algorithm executes. We also evaluate our generalization of the LPA update rule that takes into account, with varying strength, connections to the neighborhood of a node considering a new label. Experiments on computer generated networks and a wide range of social networks show that our new rule improves the quality of the detected communities compared to those found by the original LPA. The benefit of considering positive neighborhood strength is pronounced especially on real-world networks containing sufficiently large fraction of nodes with high clustering coefficient.