NodeTrix-multiplex: Visual analytics of multiplex smallworld networks

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.