An interactive graphical method for community detection in network data

Andee Kaplan (), Heike Hofmann () and Daniel Nordman ()
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Andee Kaplan: Iowa State University
Heike Hofmann: Iowa State University
Daniel Nordman: Iowa State University

Computational Statistics, 2017, vol. 32, issue 2, No 7, 535-557

Abstract: Abstract The detection of community structures within network data is a type of graph analysis with increasing interest across a broad range of disciplines. In a network, communities represent clusters of nodes that exhibit strong intra-connections or relationships among nodes in the cluster. Current methodology for community detection often involves an algorithmic approach, and commonly partitions a graph into node clusters in an iterative manner before some stopping criterion is met. Other statistical approaches for community detection often require model choices and prior selection in Bayesian analyses, which are difficult without some amount of data inspection and pre-processing. Because communities are often fuzzily-defined human concepts, an alternative approach is to leverage human vision to identify communities. The work presents a tool for community detection in form of a web application, called gravicom, which facilitates the detection of community structures through visualization and direct user interaction. In the process of detecting communities, the gravicom application can serve as a standalone tool or as a step to potentially initialize (and/or post-process) another community detection algorithm. In this paper we discuss the design of gravicom and demonstrate its use for community detection with several network data sets. An “Appendix” describes details in the technical formulation of this web application built on the R package Shiny and the JavaScript library D3.

Keywords: Graph layout; Interactive graphics; Web application; Human perception (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s00180-016-0663-5

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