Call for papers for the special issue of Information Visualization journal on “Big Graph Visual Analytics”
- Submission due: Monday, February 8, 2016
- Acceptance notifications: Monday, May 9, 2016
- Final revisions due: Monday, July 18, 2016
- Publication: October 2016
The special issue of Information Visualization will explore the technical challenges and technology development opportunities of graph visual analytics arising from the trend of big data. Today’s graph problems are increasingly multi-faceted and multi-disciplinary in nature. Many cutting-edge R&D efforts are conducted independently in disparate domains such as bioinformatics, cybersecurity, and predictive machine learning. Although technology transfers in big graph visualization are recognized and growing, many R&D problems and challenges have remained unsolved.
We invite researchers and practitioners with different interests to submit original R&D articles to this special issue of Information Visualization. We agree that the graph size that seems big today is different from what seemed big only a few years ago. While the call-for-papers doesn’t specify upper or lower bounds on the graph’s size, we are particularly interested in emerging problems that challenge conventional wisdom in computation and interaction brought by the latest “web-scale” graphs (see http://www.pdl.cmu.edu/SDI/2013/slides/big_graph_nsa_rd_2013_56002v1.pdf). Some of the pressing big graph visual analytics challenges are:
- What if graph analytics did not start with visualization? What if that happened downstream in the workflow?
- Some graphs have attributes on the edges; some have multiple millions edges between two vertices. In what way does the “flavor” of graph affect the analytics that are possible?
- Some edge attributes naturally form class hierarchies. How can this help the visual analytics process?
- For those graphs with temporal and/or geo-spatial information associated with the vertices and/or edges, how can this information improve the analytics?
- What metrics or techniques are best at showing a summary of the graph data?
- If I have a system with only 1TB of memory, how can I perform visual analytics on a graph that requires 20TB to represent it?
- In database parlance, Extract, Transform, and Load (ETL) may account for over 80% of the effort required to manipulate data. Are there ways to improve ETL using graph visualization and analytics techniques? Can better ETL be used to improve big graph visual analytics?
- Can 10,000 users concurrently run visual analytics software aimed at the same graph data source?
- Can big graph data be viewed across a network? For example, can a user in Europe run a visual analytics tool on data stored in South America?
- Pak Chung Wong, Pacific Northwest National Laboratory, email@example.com
- David Haglin, Pacific Northwest National Laboratory, firstname.lastname@example.org
- David Trimm, U.S. Department of Defense, email@example.com
Original R&D articles submitted to this special issue should be eight to ten journal pages long, where one journal page is ~800 words. Figures and tables should be accounted for accordingly with respect to the paper length. Please visit https://us.sagepub.com/en-us/nam/journal/information-visualization#submission-guidelines for information on preparing and uploading manuscripts.