Community Detection in Interval-Weighted Networks

TL;DR

This paper introduces Interval-Weighted Networks (IWN) for social network analysis, extending modularity and Louvain algorithm to account for edge weight variability, and applies it to Portuguese commuter data to identify regional communities.

Contribution

It develops a novel IWN framework and adapts community detection algorithms to handle interval-based weights, providing more accurate analysis of variable network data.

Findings

Identified three main communities in Portuguese regions.

Different community partitions emerge depending on the detection method.

Method accounts for variability in edge weights, reducing information loss.

Abstract

In this paper we introduce and develop the concept of Interval-Weighted Networks (IWN), a novel approach in Social Network Analysis, where the edge weights are represented by closed intervals composed with precise information, comprehending intrinsic variability. We extend IWN for both Newman's modularity and modularity gain and the Louvain algorithm (LA), considering a tabular representation of networks by contingency tables. We apply our methodology in a real-world commuter network in mainland Portugal between the twenty three NUTS 3 regions. The optimal partition of regions is developed and compared using two new different approaches, designated as ``Classic Louvain'' (CL) and ``Hybrid Louvain'' (HL), which allow taking into account the variability observed in the original network, thereby minimizing the loss of information present in the raw data. Our findings suggest the division of the twenty three Portuguese regions in three main communities. However, we find different geographical partitions according to the community detection methodology used. This analysis can be useful in many real-world applications, since it takes into account that the weights may vary within the ranges, rather than being constant.