Evaluating Graph Vulnerability and Robustness using TIGER
This work addresses the problem of reproducibility and research development in network robustness for researchers and practitioners, though it is incremental as it provides a toolbox rather than a new method.
The authors tackled the lack of comprehensive open-source tools for evaluating graph vulnerability and robustness by developing TIGER, a Python toolbox that includes 22 robustness measures, 17 attack strategies, 15 defense techniques, and 4 simulation tools, which has been integrated into educational resources reaching over 1,000 students.
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