  The emphasis on optimal operations and efficiency has led to increased
complexity in engineered systems. This in turn increases the vulnerability of
the system. However, with the increasing frequency of extreme events,
resilience has now become an important consideration. Resilience quantifies the
ability of the system to absorb and recover from extreme conditions. Graph
theory is a widely used framework for modeling complex engineered systems to
evaluate their resilience to attacks. Most existing methods in resilience
analysis are based on an iterative approach that explores each node/link of a
graph. These methods suffer from high computational complexity and the
resulting analysis is network specific. To address these challenges, we propose
a graph neural network (GNN) based framework for approximating node resilience
in large complex networks. The proposed framework defines a GNN model that
learns the node rank on a small representative subset of nodes. Then, the
trained model can be employed to predict the ranks of unseen nodes in similar
types of graphs. The scalability of the framework is demonstrated through the
prediction of node ranks in real-world graphs. The proposed approach is
accurate in approximating the node resilience scores and offers a significant
computational advantage over conventional approaches.
