GCRL: a graph neural network framework for network connectivity robustness learning

The resilience and adaptability of complex networks is crucial in ensuring their functionality against disruptions. Particularly, maintaining network connectivity under various attack scenarios is a key aspect of such resilience. Network connectivity refers to the degree to which nodes within a network are interconnected and able to exchange information or resources. Its robustness reflects the ability of a network to maintain connectivity under various attacks. Such ability has profound physical significance, ensuring the stability and reliability of real-world systems. Currently, connectivity robustness assessments rely heavily on very time-consuming attack simulations. This paper introduces a graph neural network framework for network connectivity robustness learning (GCRL) to advance the study of network connectivity robustness. GCRL transforms initial degree distributions and network topology into informative embedding vectors, which are then processed by a robustness learning module mainly composed of multi-layer perceptron, achieving both high speed and precision. Our extensive experiments demonstrate the superior performance of GCRL obtained in less time compared to existing methods, especially in tough scenarios where test data distributions significantly differ from the training set. The framework also shows adaptability to networks of different sizes, making it a more generalized solution for complex network robustness learning.