论文标题
使用老虎评估图形脆弱性和鲁棒性
Evaluating Graph Vulnerability and Robustness using TIGER
论文作者
论文摘要
网络鲁棒性在我们对复杂互连系统（例如运输，通信和计算机网络）的理解中起着至关重要的作用。尽管在网络鲁棒性领域进行了重大研究，但目前尚无全面的开源工具箱来帮助研究人员和从业人员参与这一重要主题。这种缺乏可用工具阻碍了可重复性和对现有工作的检查，新研究的发展以及新思想的传播。我们为Tiger贡献了一种开源的Python工具箱，以应对这些挑战。 Tiger包含22种图形鲁棒性测量，并具有原始版本和快速近似版本。 17失败和攻击策略； 15基于启发式和优化的防御技术；和4个仿真工具。通过使研究网络鲁棒性所需的工具民主化，我们的目标是帮助研究人员和从业人员分析自己的网络；并促进该领域的新研究的发展。老虎已被整合到全球教育工作者可用的NVIDIA数据科学教学套件中；以及佐治亚理工学院的数据和视觉分析课程，有1000多名学生。 Tiger开放源于：https：//github.com/safreita1/tiger
Network robustness plays a crucial role in our understanding of complex interconnected systems such as transportation, communication, and computer networks. While significant research has been conducted in the area of network robustness, no comprehensive open-source toolbox currently exists to assist researchers and practitioners in this important topic. This lack of available tools hinders reproducibility and examination of existing work, development of new research, and dissemination of new ideas. We contribute TIGER, an open-sourced Python toolbox to address these challenges. TIGER contains 22 graph robustness measures with both original and fast approximate versions; 17 failure and attack strategies; 15 heuristic and optimization-based defense techniques; and 4 simulation tools. By democratizing the tools required to study network robustness, our goal is to assist researchers and practitioners in analyzing their own networks; and facilitate the development of new research in the field. TIGER has been integrated into the Nvidia Data Science Teaching Kit available to educators across the world; and Georgia Tech's Data and Visual Analytics class with over 1,000 students. TIGER is open sourced at: https://github.com/safreita1/TIGER
