NodeTrix-Multiplex: Visual Analytics of Multiplex Small World Networks

In the proceedings of Complex Networks & Their Applications, 2017

Abstract:

Analyzing multiplex small world networks (SWNs) using community detection (CD) is a challenging task. We propose the use of visual analytics to probe and extract communities in such networks, where one of the layers defines the network topology and exhibits small-world property. Our novel visual analytics framework, NodeTrix-Multiplex (NTM), for visual exploration of multiplex SWNs, integrates focus+context network visualization, and analysis of community detection results, within the focus. We propose a heterogeneous data model, which composites multiple layers for the focus and context and thus, enables finding communities across layers. We perform a case-study on a co-authorship (collaboration) network, with a functional layer obtained from the author-topic similarity graph. We also perform an expert user evaluation of the tool, developed using NTM.

PDF DOI

To cite: Shivam Agarwal, Amit Tomar, Jaya Sreevalsan-Nair, "NodeTrix-Multiplex: Visual Analytics of Multiplex Small World Networks" In the proceedings of Complex Networks & Their Applications, 2017. 10.1007/978-3-319-50901-3_46

BibTeX:

@InProceedings{Agarwal2017Nodetrix,
author = {Agarwal, Shivam and Tomar, Amit and Sreevalsan-Nair, Jaya},
title = {{NodeTrix-Multiplex}: Visual Analytics of Multiplex Small World Networks},
booktitle = {Complex Networks \& Their Applications},
year = {2017},
publisher = {Springer International Publishing},
pages = {579--591},
abstract = {Analyzing multiplex small world networks (SWNs) using community detection (CD) is a challenging task. We propose the use of visual analytics to probe and extract communities in such networks, where one of the layers defines the network topology and exhibits small-world property. Our novel visual analytics framework, NodeTrix-Multiplex (NTM), for visual exploration of multiplex SWNs, integrates focus+context network visualization, and analysis of community detection results, within the focus. We propose a heterogeneous data model, which composites multiple layers for the focus and context and thus, enables finding communities across layers. We perform a case-study on a co-authorship (collaboration) network, with a functional layer obtained from the author-topic similarity graph. We also perform an expert user evaluation of the tool, developed using NTM.},
isbn = {978-3-319-50901-3},
doi = {10.1007/978-3-319-50901-3\_46}
}