Using Dual-Network Analyser for extracting communities from Dual Networks

TL;DR

This paper introduces a Python-based graphical tool for analyzing dual networks, enabling visualization and community detection using adapted algorithms, demonstrated on social, biological, and co-authorship networks.

Contribution

The paper presents a novel graphical user interface and adapted algorithms for community detection in dual networks, enhancing analysis capabilities.

Findings

Efficient extraction of dense subgraphs and communities from dual networks.

The tool is effective across social, biological, and co-authorship networks.

The GUI provides a valuable innovation for dual network analysis.

Abstract

The representation of data and its relationships using networks is prevalent in many research fields such as computational biology, medical informatics and social networks. Recently, complex networks models have been introduced to better capture the insights of the modelled scenarios. Among others, dual networks -based models have been introduced, which consist in mapping information as pair of networks containing the same nodes but different edges. We focus on the use of a novel approach to visualise and analyse dual networks. The method uses two algorithms for community discovery, and it is provided as a Python-based tool with a graphical user interface. The tool is able to load dual networks and to extract both the densest connected subgraph as well as the common modular communities. The latter is obtained by using an adapted implementation of the Louvain algorithm. The proposed algorithm and graphical tool have been tested by using social, biological, and co-authorship networks. Results demonstrate that the proposed approach is efficient and is able to extract meaningful information from dual networks. Finally, as contribution, the proposed graphical user interface can be considered a valuable innovation to the context.