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With the burgeoning advancements of computing and network communicationtechnologies, network infrastructures and their application environments havebecome increasingly complex. Due to the increased complexity, networks are moreprone to hardware faults and highly susceptible to cyber-attacks. Therefore,for rapidly growing network-centric applications, network resilience isessential to minimize the impact of attacks and to ensure that the networkprovides an acceptable level of services during attacks, faults or disruptions.In this regard, this thesis focuses on developing effective approaches forenhancing network resilience. Existing approaches for enhancing networkresilience emphasize on determining bottleneck nodes and edges in the networkand designing proactive responses to safeguard the network against attacks.However, existing solutions generally consider broader application domains andpossess limited applicability when applied to specific application areas suchas cyber defense and information diffusion, which are highly popularapplication domains among cyber attackers.  This thesis aims to design effective, efficient and scalable techniques fordiscovering bottleneck nodes and edges in the network to enhance networkresilience in cyber defense and information diffusion application domains. Wefirst investigate a cyber defense graph optimization problem, i.e., hardeningactive directory systems by discovering bottleneck edges in the network. Wethen study the problem of identifying bottleneck structural hole spanner nodes,which are crucial for information diffusion in the network. We transform bothproblems into graph-combinatorial optimization problems and design machinelearning based approaches for discovering bottleneck points vital for enhancingnetwork resilience.

Enhancing Network Resilience through Machine Learning-powered Graph  Combinatorial Optimization: Applications in Cyber Defense and Information  Diffusion