Scalable Graph Neural Network-based framework for identifying critical nodes and links in Complex Networks

TL;DR

This paper introduces a scalable GNN-based framework for efficiently identifying critical nodes and links in large complex networks, overcoming computational limitations of traditional methods.

Contribution

It presents a novel GNN model that learns criticality scores from small samples and predicts for large networks, enhancing scalability and efficiency.

Findings

Accurately predicts critical nodes/links in large networks.

Offers significant computational advantages over traditional methods.

Validated on synthetic and real-world networks.

Abstract

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.