NSF CAREER: Network Resilience
Networks are collections of interdependent and dynamic components that make up many crucial systems of various scales, such as the social systems, food supply chains, and the neural networks in our brain. However, these networks can be highly vulnerable to failures. A small disturbance may bring the whole network across a tipping point and shift it abruptly and irreversibly to an undesired state, resulting in large catastrophic collapses. Examples are mass extinctions in ecological networks, cascading failures in power grids, and social convention changes in human and animal networks. Resilience is the ability of networked systems to adjust their activity to retain basic functionality and avoid large shifts in the face of internal disturbances and environmental changes. The cost of resilience loss sometimes is unaffordable: The outbreak of the COVID-19 pandemic has caused over two million deaths worldwide as of January 25, 2021, and continues to kill increasingly more people and shut down increasingly more economic activities. This award aims to develop a universal theoretical and practical foundation for the resilience of complex networked systems, for systems from different fields, such as biology, ecology, transportation, and many more. This award will design fast and accurate algorithms to predict a system's resilience even when only partial information is known, offering ways to prevent the collapse of ecological, biological, or economic systems and guide the design of technological systems resilient to both internal failures and environmental changes. Students, including K-12, will be engaged in learning about networks and resilience in multiple ways, ranging from research opportunities to lectures and a cyber-attack game design, where the defender aims to create a resilient network with limited recourses to withstand the attacker's strategic damages.
The technical aims of the project are divided into three thrusts. The first thrust creates a unified theory that captures a universal resilience behavior of different systems by relaxing some assumptions and recognizing different types of interactions between the dynamical components and among interacting systems. The second thrust designs fast and accurate algorithms for predicting systems' resilience with incomplete information on the topology or dynamics. The last thrust develops mathematical tools for resilience enhancement through topology adaptation and state-based control to characterize their readiness during the operation trajectory and throughout the recovery process, enabling sustainable, recoverable, and resilient system design. These three research thrusts will be validated by a comprehensive evaluation of the progress, including computer simulations and real-world experiments. This research effort will produce the foundation for network resilience that allows prediction and controlling it with sufficient generality to apply it to many areas where resilience to adverse perturbations is of paramount concern. This CAREER project enables a paradigm shift in how network resilience is currently understood, yielding transformative ideas to enhance how networked systems can best be built and managed (human-made systems) or self-organized and adapted (natural systems) to enhance their resilience to internal failures and external environmental changes.
Jianxi Gao (PI)
Jinzhu Yu (postdoc)
Manqing Ma (PhD)
Cheng Ma (PhD)
 C. Jiang, B. K. Szymanski, J. Lian, S. Havlin, and J. Gao. "Nuclear reaction network unveils novel reaction patterns based on stellar energies." New Journal of Physics 23, no. 8 (2021): 083035.
 H. Deng, D. P. Aldrich, M. M. Danziger, J. Gao, N. E. Phillips, S. P. Cornelius, and Q. Wang. "High-resolution human mobility data reveal race and wealth disparities in disaster evacuation patterns." Humanities and Social Sciences Communications 8, no. 1 (2021): 1-8.
 L. Zhong, M Diagne, W. Wang, and J. Gao (2021) "Country distancing reveals the effectiveness of travel restrictions during COVID-19." Communications Physics, (2021)4:121, https://doi.org/10.1038/s42005-021-00620-5.
 Y. Gao, S. Chen, J. Zhou, H. E. Stanley, & J. Gao. Percolation of edge-coupled interdependent networks. Physica A: Statistical Mechanics and its Applications, 126136, (2021).
 X. Meng, J. Gao, and S. Havlin. "Concurrence Percolation in Quantum Networks." Physical Review Letters, 126.17 (2021): 170501.