Critical infrastructure networks are essential engineered systems for modern society, encompassing power grids, telecommunication networks, transportation systems, and water distribution networks, among others. Despite their crucial role in maintaining community normalcy, critical infrastructure networks face a variety of threats, such as aging, extreme weather, intentional attacks, chronic hazards, and catastrophic disasters. Any damage to critical infrastructure networks may be significantly amplified by their interdependencies and the increasing interactions with users and operators, ultimately leading to serious economic losses and potential loss of life. As enhancing the reliability and resilience of critical infrastructure systems is a priority for governance and society’s wellbeing,, different fields of research and practice are needed to develop ideas, test them, and implement them. Traditionally, efforts to improve the reliability and resilience of engineered infrastructure systems have focused on individual components within the systems, such as retrofitting and hardening of bridges in highway networks or substations in power grids. However, infrastructure systems are interconnected in network form, and decisions for retrofit or hardening should consider the broader network perspective. Furthermore, infrastructure systems are interconnected among each other, forming a network of networks (NoNs) due to the existence of cross-network dependencies. Thus, it is necessary to investigate the reliability and resilience of infrastructure systems from a network-level perspective. Network science is a dynamic field that has developed sophisticated techniques and algorithms to investigate the properties of networks, structures in networks, and processes on networks. However, the development of advanced techniques inspired by network science to complex problems in engineered infrastructure systems is still limited. We leverage significant developments in algorithms and techniques from network science and understanding of infrastructure systems to benefit their reliability and resilience. Currently, exact reliability computation methods and optimization-based methods are among the most effective strategies for reliability and resilience management of infrastructure systems, due to their guarantees on the accuracy or optimality of the solution. However, the computational complexity of these algorithms for reliability and resilience, is usually beyond affordable. The conflict between desirable models and practical limitations calls for a rational framework which allows decision-makers to choose an effective alternative option when the optimal solution is not feasible. We address this tension with Simon’s theory of satisficing. By selecting a solution that is ‘good enough’ rather than optimal, satisficing theory is applied to the reliability and resilience of engineered infrastructure systems, on problems such as reliability estimators with guarantees on interval and confidence, and close-to-optimal solutions for optimization models, among others. Inspired by the satisficing theory, we build upon techniques and algorithms from network science and proposed algorithms on multiple computationally intractable problems related to the reliability and resilience of infrastructure systems. Funding allocation on retrofit of bridges in highway networks are usually heavily impacted by the influence of politics, which is at odds to the optimal solution from engineering perspective. Our proposed socio-technical ranking algorithm based on a framework inspired by the Katz centrality from network science can effectively integrate network topology, bridge vulnerability information and impact of politics, and provide a compromise between optimality and practicality. We also proposed a principled recovery algorithm based on network partitioning and percolation process in networks, to restore damaged infrastructure systems quickly with supply-demand balance, which is similar to solutions from mixed-integer optimization models. In addition, we introduce the dimension of resilience into the network dismantling problem in network science to identify a dismantling solution that not only breaks the network, but also delays its recovery. By developing an adversarial-dismantling-retrofit strategy, we reveal critical information for long-term resilience enhancement of infrastructure systems. Finally, we investigate the functional relationship between different components of a transportation network, by extracting its dynamical backbones from real-time traffic data, which supports congestion mitigation, traffic intervention and transportation network design. Overall, inspired by community reliability and resilience challenges, my research builds upon network science methods to develop novel algorithms that address resource allocation to bridges in transportation networks, resilience-based restoration using distributed percolation process, resilience-informed network dismantling algorithms and adversarial-dismantling-retrofit strategies, along with mechanisms of resilience for infrastructure systems via functional decompositions to support dynamics-based design and operation. All these algorithms are implementable in practice, unlike their parent methods which remain intractable for large infrastructure problems of today. By being integrated to the Interdependent Networked Community Resilience Modeling Environment (IN-CORE) platform developed by the National Institute of Standards and Technology (NIST), our proposed algorithms will be implemented to support community resilience. Furthermore, as network science is also at the intersection of physical systems and their information processing capabilities, rendering insights from this research useful for emerging developments in system automation, decentralized consensus, and the development of quantum algorithms implementable in noisy-intermediate scale quantum devices.