Authors:
Jaya Sreevalsan-Nair
and
Shivam Agarwal
Affiliation:
International Institute of Information Technology, India
Keyword(s):
Small-world Networks, NodeTrix, Similarity Matrix, Hierarchical Communities, Workflow, Visual Analytics, Clustering Algorithm.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
Graph Visualization
;
Visual Analytical Reasoning
;
Visual Data Analysis and Knowledge Discovery
;
Visual Representation and Interaction
Abstract:
While there are several visualizations of the small world networks (SWN), how does one find an appropriate
set of visualizations and data analytic processes in a data science workflow? Hierarchical communities in
SWN aid in managing and understanding the complex network better. To enable a visual analytics workflow
to probe and uncover hierarchical communities, we propose to use both the network data and metadata (e.g.
node and link attributes). Hence, we propose to use the network topology and node-similarity graph using
metadata, for knowledge discovery. For the construction of a four-level hierarchy, we detect communities on
both the network and the similarity graph, by using specific community detection at specific hierarchical level.
We enable the flexibility of finding non-overlapping or overlapping communities, as leaf nodes, by using spectral
clustering. We propose NodeTrix-CommunityHierarchy (NTCH), a set of visual analytic techniques for
hierarchy construction, visual explo
ration and quantitative analysis of community detection results. We extend
NodeTrix-Multiplex framework (Agarwal et al., 2017), which is for visual analytics of multilayer SWN,
to probe hierarchical communities. We propose novel visualizations of overlapping and non-overlapping communities,
which are integrated into the framework. We show preliminary results of our case-study of using
NTCH on co-authorship networks.
(More)