Community Detection in Directed Weighted Networks using Voronoi Partitioning

TL;DR

This paper introduces a Voronoi partitioning-based algorithm for community detection in directed, weighted networks, effectively handling dense graphs and edge weights representing lengths, with applications in brain connectivity and transportation networks.

Contribution

The paper presents a novel community detection algorithm specifically designed for directed, weighted networks that directly uses edge lengths and detects hierarchical structures.

Findings

Effective in dense graphs with length-based weights

Able to detect hierarchical network structures

Comparable efficiency to state-of-the-art algorithms

Abstract

Community detection is a ubiquitous problem in applied network analysis, yet efficient techniques do not yet exist for all types of network data. Most techniques have been developed for undirected graphs, and very few exist that handle directed and weighted networks effectively. Here we present such an algorithm based on Voronoi partitionings. As an added benefit, this method can directly employ edge weights that represent lengths, in contrast to algorithms that operate with connection strengths, requiring ad-hoc transformations of length data. We demonstrate the method on inter-areal brain connectivity, air transportation networks, as well as on randomly generated benchmark networks. The algorithm can handle dense graphs where weights are the main factor determining communities. The hierarchical structure of networks can also be detected, as shown for the brain. Its time efficiency is comparable with other state-of-the-art algorithms, the most costly part being Dijkstra's shortest paths algorithm.