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.
Identifying critical nodes and links in graphs is a crucial task. These nodes/links typically represent critical elements/communication links that play a key role in a system's performance . However, a majority of the methods available in the literature on the identification of critical nodes/links are based on an iterative approach that explores each node/link of a graph at a time, repeating for all nodes/links in the graph. Such methods suffer from high computational complexity and the resulting analysis is also network-specific. To overcome these challenges, this article proposes a scalable and generic graph neural network (GNN) based framework for identifying critical nodes/links in large complex networks. The proposed framework defines a GNN based model that learns the node /link criticality score on a small representative subset of nodes /links. An appropriately trained model can be employed to predict the scores of unseen nodes /links in large graphs and consequently identify the most critical ones . The scalability of the framework is demonstrated through prediction of nodes/links scores in large scale synthetic and real-world networks . The proposed approach is fairly accurate in approximating the criticality scores and offers a significant computational advantage over conventional approaches.
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The emphasis on optimal operations and efficiency has led to increased complexity in engineered systems. This in turn increases the vulnerability of the system
Identifying critical nodes and links in graphs is a crucial task. These nodes/links typically represent critical elements/communication links that play a key role in a system's performance
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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
a majority of the methods available in the literature on the identification of critical nodes/links
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. These
at a time, repeating for all nodes/links in the graph. Such
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network specific. To address
also network-specific. To overcome
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we propose a
this article proposes a scalable and generic
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approximating node resilience
identifying critical nodes/links
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based
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rank
/link criticality score
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. Then, the
/links. An appropriately
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ranks
scores
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in similar types of graphs
/links in large graphs and consequently identify the most critical ones
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the prediction of node ranks in
prediction of nodes/links scores in large scale synthetic and
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graphs
networks
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fairly
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node resilience
criticality