VISUALIZATION: Visualization of Giga-Graphs and Graph Processes

图形绘制和信息可视化工具在许多领域是必不可少的，包括电信、数据库和软件工程，其中模型包含数百万个对象和关系。尽管图形绘制是一门丰富的学科，但该领域中有前景的新概念和算法在应用领域的实际影响太少。这个项目提出了一个综合的研究和教育计划，重点是大数据集和图形处理的图形绘制和信息可视化技术。这项工作的研究部分侧重于设计和实现高效的数据结构和算法，用于大型图形的可视化，目标是影响应用领域的现实问题。这个问题尤其重要，因为最近的技术进步带来了数据量的增加和数据复杂性的增加。这项工作的理论方面包括设计可伸缩的力导向方法、分层图分解、图处理的替代视觉表示、外部存储算法和支持高效导航操作的数据结构。理论结果将用于开发实用工具，这些工具将打包在一个软件库和一个自适应图形可视化环境AGAVE中，该环境使用新的图形建模标准GraphML。通过工业合作，新方法将使用真实世界的数据进行校准和测试。还将研究动态图形过程可视化的替代图形表示方法和技术。该提案的教育部分包括：(1)创建和管理已获得资助的新图形和可视化实验室；(2)设计和实现用于大图形和图形过程可视化的教学工具；(3)研究生和本科生的指导；(4)积极参与外联和综合活动。

Graph drawing and information visualization tools are essential in many fields, including telecommunications,databases, and software engineering, where the models contain millions of objects and relationships. Even though graph drawing is a rich discipline, the promising new concepts and algorithms in the field have had too little practical impact in application areas. This project presents an integrated research and education plan focusing on graph drawing and information visualization techniques for large data sets and graph processes. The research component of this work focuses on the design and implementation of efficient data structures and algorithms for visualization of large graphs with the goal of impacting real-world problems in application areas. This problem is especially relevant because recent technological advances have brought about increased data volumes and increased data complexity. The theoretical aspects of this work include designing scalable force-directed methods, hierarchical graph decompositions, alternative visual representations for graph processes, external memory algorithms, and data structures that support efficient navigation operations. The theoretical results will be used to develop practical tools that will be packaged in a software library and an Adaptive GrAph Visualization Environment, AGAVE, that uses the new graph modeling standard GraphML. Through industrial collaborations, the new methods will be calibrated and tested using real-world data.Alternatives graph representation methods and techniques for visuazalizing dynamic graph processes will also be investigated. The educational component of this proposal includes: (1) Creation and management of the already funded new Graphics and Visualization Lab; (2) Design and implementation of educational tools for visualization of large graphs and graph processes; (3) Supervision of graduate and undergraduate students; (4) Active involvement in outreach and integrative activities.