Yu Zhang, Haowei Chen, Qiyu Chen, Jie Ding, Xiang Li
{"title":"GCRL: a graph neural network framework for network connectivity robustness learning","authors":"Yu Zhang, Haowei Chen, Qiyu Chen, Jie Ding, Xiang Li","doi":"10.1088/1367-2630/ad6ead","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":19181,"journal":{"name":"New Journal of Physics","volume":"2 1","pages":""},"PeriodicalIF":2.8000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"New Journal of Physics","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1088/1367-2630/ad6ead","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
New Journal of Physics publishes across the whole of physics, encompassing pure, applied, theoretical and experimental research, as well as interdisciplinary topics where physics forms the central theme. All content is permanently free to read and the journal is funded by an article publication charge.