New Project: MiniVAN for the Visual Analysis of Networks
March 30, 2018
The Public Data Lab has received funding from Sage Publishing to develop a project called MiniVAN. This will be a simple tool to facilitate the visual analysis of networks and online publication of results. Further details are copied below.
MiniVAN will be an easy-to-use tool that will support non-specialist social scientists in the visual analysis of their networks and in the online publication of their results.
Networks are becoming increasingly popular in the social sciences as interfaces for exploratory data analysis. The “Visual Analysis of Networks” (VAN) allows academics to explore large relational datasets without having to deal with the full complexity of graph mathematics. A key barrier remains, however, for the adoption of this approach: current VAN tools are either too complicated or unable handle the growing size of the datasets that are typical in the digital social sciences.
MiniVAN aims to solve this problem by providing a tool for the visual analysis of networks that is accessible to academics with little knowledge of mathematics or coding and yet able to scale up to output graphs containing hundreds of thousands of nodes.
MiniVAN is being developed by Tommaso Venturini, Jonathan Gray and Guillaume Pique from the Public Data Lab (PDL), a European network of researchers which seeks to facilitate research, democratic engagement and public debate around the future of the data society. SAGE Publishing partnered with the Institute for Policy Research at the University of Bath to support the establishment of the Public Data Lab in 2017.
The MiniVAN project will draw on the team’s previous open source projects, including Gephi, Sigmajs and Graphology – and will form part of this ecosystem of tools. In line with the Public Data Lab’s spirit of openness, the PDL is seeking to develop MiniVAN in collaboration with the digital social science community. If you have any ideas or needs for this tool, please get in touch via firstname.lastname@example.org