Academic journal article Journal of Social Structure

Using Visualizations to Explore Network Dynamics

Academic journal article Journal of Social Structure

Using Visualizations to Explore Network Dynamics

Article excerpt


This study was supported by NIH grant number 1R01CA157577 (PI: Thomas W. Valente) and training grant T32CA009492 from the National Cancer Institute/National Institutes of Health. An earlier version was presented at the XXXIII annual INSNA meeting, Hamburg, Germany. We would like to thank the International Union for Cancer Control for its assistance with this study.

1. Introduction#

Visualization tools have been a strong component of scientific progress in various fields. In many cases, data summarized as graphs or charts can help clearly represent ideas. In other examples, concepts have become associated with a particular image that originated from research, from the miniscule double helix twisted ladder of DNA to the large spiral arms of the Milky Way. A great deal of information can be derived from simple images, whether viewing a line graph of company stock or a pyramid view of a food chain.

Network graphs, in particular, are a useful tool that can help model relations, summarize data, and represent abstract concepts in a clear and intuitive way. The value of using network graphs to visualize data has been applied in different fields, and has helped improve our knowledge of disease spread (Christakis and Fowler 2010), international telecommunications (Barnett 2001), ecological systems (Stefano, Alonso and Pascual 2008), social networks (Moody and White 2003), health studies (Valente 2010), among many others. Social network analysis (SNA) often uses a sociogram to clarify different concepts. Sociograms are network graphs in which nodes represent actors and ties represent relationships between them.

The sociogram is a powerful analysis tool, helping researchers identify points of interest such as clusters (Newman and Girvan 2004), boundary spanners (Levina and Vaast 2005), central and peripheral layers (Borgatti and Everett 2000), and other structural properties that otherwise would not be obvious in numeric data (e.g. an adjacency matrix). Today, there are online communities that form around every conceivable topic, so it is no surprise that SNA has become popular for online social network research.

Growing in parallel with SNA is the availability of different software tools. Since Moreno's (1932) small hand drawn examples, modern computer technology can now create networks with 10's of millions of users (Mislove, Massimiliano, Gummadi, Drushel and Bhattacharjee 2007). The development of SNA software has aided SNA research, as increased computing power has enabled fast complex calculations and supported large-scale network analyses (e.g. visualizing million node networks). Researchers can conduct studies based on network structures, and many of the calculations and measurements are made immediately available. Methodological developments are often paired alongside certain software, such as exploratory analysis using Pajek (de Nooy, Mrvar and Batagelj 2005). Other software packages each have their own benefits, such as UCINet's1 easy support of many SNA tools, or the statnet package built into the freely available R environment2, offering great flexibility and statistical analyses.

Given the power of SNA, there are still gaps that have only recently started to be addressed. For example, sociograms are, by nature, static representations. They are snapshots of a network in a single moment in time, giving no hints as to how or why the network developed into a particular structure, or what it could potentially become. More studies into the evolution of social networks would be beneficial for research, especially in online communities, which can grow at tremendous speeds.

This paper applies SNA and dynamic network visualizations to study the growth and evolution of GLOBALink, an online network focused on global tobacco control. In analyzing GLOBALink data collected over a 20-year period, we are not only able to visualize the membership network over time, but can also link shifts in the network to major political, social, and economic changes that occurred in the global tobacco control community. …

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