Big data presents both opportunities and challenges to all fields of study and practice. Visualization has been proven effective as a knowledge discovery and storytelling tool for big data. This project aims to develop new visualization technologies for big network data that will both illustrate empirical findings and generate new discoveries. Although many network visualization techniques and tools have been introduced, visualizing large, dynamic networks to extract key entities, structures, and trends from the network data remains a challenging task. Most of the existing network visualization solutions were not designed for handling dynamic networks and are too slow for interactive exploration of large networks. This project will closely examine the integral parts of a holistic solution for the problem of big network visualization. The research study will be largely driven by the data analysis needs of sociological studies such as finding hidden associations between multiple networks; however, the project team will also investigate the solution's applicability in areas such as emergency management, life science and cyber security. The resulting technologies are expected to drastically enhance one's ability to explore and understand large, complex dynamic networks for knowledge discovery, critical decision making, and storytelling. This research effort is timely because of the explosive growth of data and common use of graphs as both the internal data structure and a visual representation in data-driven applications. Those who must deal with large, complex dynamic network data for their work will benefit from the advanced visualization technologies resulted from this research project. Students participating in this project will acquire strong interdisciplinary research skills for real-world problem solving.
This research underscores the importance of providing a comprehensive solution to the understanding of big data containing complex relations, structure, and trends. Primary research topics are: (1) Visual depiction and exploration of big network data; (2) Modeling and visualizing dynamic network data; (3) Visual monitoring and analysis of live, streaming network data; and (4) Provenance and storytelling with dynamic network data. This project will explore and integrate new network modeling, reduction, and visualization techniques for analyzing large, multivariate dynamic graphs. The resulting research innovations will both enhance existing methods and investigate new approaches to dynamic network visual analytics and drastically improve their usability for real-world applications. The targeted applications, emergency service and sociology, present the project team with some of the most challenging problems to address in making sense of heterogeneous dynamic big networks data. The collaborating domain experts are fully committed to participating in the evaluation work, which promises to produce usable technologies that will enable respondents to look at the data in new ways and uncover intricate relations among different entities/events for critical decision making and mitigation planning. The project results will be disseminated to the visualization community and beyond through annual conferences, workshops, and tutorials, and also through the project website which will include project status updates and resulting images, videos, and prototype software.
