Analysis and Visualization of Complex Graphs

该项目的目标是开发能够对大型、复杂、非平面图形进行可视化探索的方法。正在开发新的算法来粗化图表，这些算法保留了图表的整体结构，同时大大降低了图表的复杂性。这些算法技术允许构建多尺度表示，从而使传统的图形分析和数据挖掘操作更加高效。它们还为复杂图形的高级可视化探索提供了基础。此外，该项目还寻求利用计算机图形硬件和渲染技术的最新进展来实时生成尽可能清晰的图形可视化，以便用户可以交互地探索和理解结构最复杂的图形结构。这些技术在蛋白质功能分析、社会网络理解、通信网络设计、数据挖掘、证券市场分析等众多重要领域有着广泛的应用。新的可视化方法在本项目开发的软件系统中实现，并在开放源码许可下发布，以帮助其他研究和开发项目。有关该项目的软件、出版物和其他信息可在(http://graphics.cs.uiuc.edu/~garland/research/graphs.html).网站上访问

The goal of this project is to develop methods enabling the visual exploration of large, complex, non-planar graphs. New algorithms are being developed for coarsening graphs that preserve the overall structure of the graph while greatly reducing its complexity. These algorithmic techniques allow for building multi-scale representations that make traditional graph analysis and data mining operations far more efficient. They also provide a basis for advanced visual exploration of complex graphs. In addition, this project is also seeking to leverage the latest advances in computer graphics hardware and rendering technology to generate graph visualizations of the greatest possible clarity in real time, so that a user can interactively explore and understand the most structurally intricate of graph structures. These technologies have broad application in many diverse and important domains, including protein function analysis, social network understanding, communication network design, data mining, and security market analysis. The new visualization methods are implemented in software systems developed in this project and released under an open source license to aid others in research and development projects. The software, publications and other information on this project can be accessed on the Web site (http://graphics.cs.uiuc.edu/~garland/research/graphs.html).