Weighted Label Propagation Algorithm based on Local Edge Betweenness

TL;DR

This paper introduces a weighted label propagation algorithm utilizing local edge betweenness to efficiently detect communities in large, weighted, and real-world networks with near linear time complexity.

Contribution

It proposes an improved label propagation algorithm that incorporates edge betweenness, enabling scalable and accurate community detection in large, weighted graphs.

Findings

Achieves near linear time complexity for community detection.

Maintains acceptable accuracy in real-world and artificial networks.

Demonstrates scalability and efficiency in large graphs.

Abstract

In complex networks, especially social networks, networks could be divided into disjoint partitions that the ratio between the number of internal edges (the edges between the vertices within same partition) to the number of outer edges (edges between two vertices of different partitions) is high. Generally, these partitions are called communities. Detecting these communities helps data scientists to extract meaningful information from graphs and analyze them. In the last decades, various algorithms have been proposed to detect communities in graphs, and each one has examined this issue from a different perspective. However, most of these algorithms have a significant time complexity and costly calculations that make them unsuitable to detect communities in large graphs with millions of edges and nodes. In this paper, we have tried to improve Label Propagation Algorithm by using edge betweenness metric, so that it is able to identify distinct communities in both real world and artificial networks in near linear time complexity with acceptable accuracy. Also, the proposed algorithm could detect communities in weighted graphs. Empirical experiments show that the accuracy and speed of the proposed algorithm are acceptable; additionally, the proposed algorithm is scalable.