  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.
