Using dual-network-analyser for communities detecting in dual networks

Pietro Hiram Guzzi,Giuseppe Tradigo,Pierangelo Veltri

doi:10.1186/s12859-022-04564-7

Pietro Hiram Guzzi, Giuseppe Tradigo + Show 1 more

Open Access

https://doi.org/10.1186/s12859-022-04564-7

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Abstract

BackgroundRepresentations of the relationships among data using networks are widely used in several research fields such as computational biology, medical informatics and social network mining. Recently, complex networks have been introduced to better capture the insights of the modelled scenarios. Among others, dual networks (DNs) consist of mapping information as pairs of networks containing the same set of nodes but with different edges: one, called physical network, has unweighted edges, while the other, called conceptual network, has weighted edges.ResultsWe focus on DNs and we propose a tool to find common subgraphs (aka communities) in DNs with particular properties. The tool, called Dual-Network-Analyser, is based on the identification of communities that induce optimal modular subgraphs in the conceptual network and connected subgraphs in the physical one. It includes the Louvain algorithm applied to the considered case. The Dual-Network-Analyser can be used to study DNs, to find common modular communities. We report results on using the tool to identify communities on synthetic DNs as well as real cases in social networks and biological data.ConclusionThe proposed method has been tested by using synthetic and biological networks. Results demonstrate that it is well able to detect meaningful information from DNs.

Highlights

Representations of the relationships among data using networks are widely used in several research fields such as computational biology, medical informatics and social network mining
We study dual network (DN) and focus on finding the Densest Connected Subgraph and the Modular Connected Subgraphs in a DN, which are both dense components of the considered graph
We focus on the analysis of modular communities and on modelling phenomena and datasets which can gain clarity, expressiveness or significance when represented as DNs

Summary

Introduction

Representations of the relationships among data using networks are widely used in several research fields such as computational biology, medical informatics and social network mining. Network-based models have been widely used as a problem-solving strategy to analyse data interactions and relations in many domains. In computational biology, network-based models are used to study relationships between biological macromolecules, and their associations [1,2,3,4]. Even social network data can be modelled with graphs and analysed to extract relevant information regarding connections (e.g., similarities, shared interests) among users [7]. To capture important attributes and to improve the mapping of real problems, a multiplex network model variant, called dual network (DN), has been defined. Where there is the need to model and study evolving phenomena [14, 15], graph pairs can be used to represent two different views of the same dataset

Results

Discussion

Conclusion