2017/05/03 by Steven B. Bradlow, Bradlow, Steven B., Konstantinos Kapenekakis +7
Computer Science · Physics and Astronomy · #Complex Network Analysis Techniques #Data Structures and Algorithms (cs.DS) #Data Visualization and Analytics #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #Web visibility and informetrics #cs.DS #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1705.01591
4 pages, 3 figures, 1 table
arxiv created 2017/05/03 · openalex publication_date 2017/05/03 · arxiv updated 2017/05/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a method for demonstrating sub community structure in scientific networks of relatively small size from analyzing databases of publications. Research relationships between the network members can be visualized as a graph with vertices corresponding to authors and with edges indicating joint authorship. Using a fast clustering algorithm combined with a graph layout algorithm, we demonstrate how to display these clustering results in an attractive and informative way. The small size of the graph allows us to develop tools that keep track of how these research sub communities evolve in time, as well as to present the research articles that create the links between the network members. These tools are included in a web app, where the visitor can easily identify the various sub communities, providing also valuable information for administrational purposes. Our method was developed for the GEAR mathematical network and it can be applied to other networks.