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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 exploration and quantitative analysis of community detection results. We extend NodeTrix-Multiplex framework [1], 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.

NodeTrix-CommunityHierarchy: Techniques for Finding Hierarchical Communities for Visual Analytics of Small-world Networks