[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119854-en":3,"doc-seo-119854-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119854,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Network Reliability Analysis through Survival Signature and Machine Learning Techniques","Complex networks are central to modern infrastructure, and failures can trigger significant societal consequences. Yet reliability analysis becomes computationally difficult as networks grow in size and structural complexity. This research presents a graph-based neural network framework to estimate survival signature and network reliability with high accuracy and efficiency. It aggregates feature information from neighboring nodes, leverages higher-order graph neural networks, and uses an adaptive framework with efficient algorithms for improved prediction accuracy, outperforming traditional machine learning approaches and enabling reliability estimation for network variants.","1 Network Reliability Analysis through Survival Signature and  \n2 Machine Learning Techniques  \n3 Yan Shi a􀀍 , Jasper Behrensdorfa, Jiayan Zhou b, Yue Hu a, Matteo Broggi a, Michael Beer a, c, d  \n4 a Institute for Risk and Reliability, Leibniz Universität Hannover, Hannover 30167, Germany  \n5 b College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China  \n6 c Institute for Risk and Uncertainty, University of Liverpool, Liverpool L69 7ZF, United Kingdom  \n7 d International Joint Research Center for Resilient Infrastructure & International Joint Research Center for  \n8 Engineering Reliability and Stochastic Mechanics, Tongji University, Shanghai 200092, China  \n9 Abstract: As complex networks become ubiquitous in modern society, ensuring their reliability is crucial  \n10 due to the potential consequences of network failures. However, the analysis and assessment of network 11 reliability become computationally challenging as networks grow in size and complexity. This research 12 proposes a novel graph-based neural network framework for accurately and efficiently estimating the 13 survival signature and network reliability. The method incorporates a novel strategy to aggregate feature 14 information from neighboring nodes, effectively capturing the response flow characteristics of networks.  \n15 Additionally, the framework utilizes the higher-order graph neural networks to further aggregate feature  \n16 information from neighboring nodes and the node itself, enhancing the understanding of network  \n17 topology structure. An adaptive framework along with several efficient algorithms is further proposed to  \n18 improve prediction accuracy. Compared to traditional machine learning-based approaches, the proposed  \n19 graph-based neural network framework integrates response flow characteristics and network topology  \n20 structure information, resulting in highly accurate network reliability estimates. Moreover, once the  \n21 graph-based neural network is properly constructed based on the original network, it can be directly used  \n22 to estimate network reliability of different network variants, i.e., sub-networks, which is not feasible with  \n23 traditional non-machine learning methods. Several applications demonstrate the effectiveness of the  \n24 proposed method in addressing network reliability analysis problems.  \n25 Keywords: Network reliability; Survival signature; Graph-based neural network; Adaptive framework;  \n26 Machine learning  \n27 1. Introduction  \n28 Complex technological networks, such as power plant networks, transportation networks,  \n􀀍 Corresponding author [E-mail: ](E-mail: yan.shi@irz.uni-hannover.de)[yan.shi@irz.uni-hannover.de](E-mail: yan.shi@irz.uni-hannover.de)  \n29 communication networks, and others, are pervasive in modern society. These networks are deeply  \n30 integrated into the infrastructure of modern society, and their failure can have serious consequences on  \n31 society's well-being. Consequently, there is a growing demand for modern technological networks to  \n32 exhibit high reliability in their operations [1]. It is therefore essential to analyze the reliability of networks, 33 which measures their ability to provide the required service while considering component or link  \n34 uncertainties, during their design and operation [2] . However, as these networks increase in size and  \n35 complexity, the analysis and assessment of their reliability require significant computational effort. This  \n36 necessitates the efficient estimation of network reliability to be of utmost importance.  \n37 Currently, the traditional approaches for calculating network reliability can be broadly classified  \n38 into four categories: enumeration methods, direct methods, decomposition methods, and simulation  \n39 methods. Enumeration methods typically involve complete state enumeration or more advanced  \n40 techniques like minimal path or minimal cut enumeration [3] . Direct methods","cbCait1OsmwfCcCk","https://ap.wps.com/l/cbCait1OsmwfCcCk","pdf",2877793,1,36,"English","en",105,"# Abstract\n# Introduction\n## Motivation: reliability in complex technological networks\n## Computational challenge: NP-hard reliability estimation\n## Existing approaches: enumeration, direct, decomposition, simulation\n# Proposed method overview","[{\"question\":\"Why is network reliability analysis difficult for large-scale complex networks?\",\"answer\":\"Network reliability estimation requires substantial computation because reliability calculation is NP-hard, making exhaustive or exact methods infeasible as networks grow in size and complexity.\"},{\"question\":\"How does the proposed framework estimate survival signature and network reliability?\",\"answer\":\"It uses a graph-based neural network that aggregates feature information from neighboring nodes and further applies higher-order graph neural networks to capture both flow characteristics and topology information.\"},{\"question\":\"What advantage does the graph-based neural network provide compared with traditional machine learning approaches?\",\"answer\":\"It integrates response flow characteristics and network topology, enabling highly accurate reliability estimates and allowing direct estimation for network variants (such as sub-networks) once constructed from the original network.\"}]","Network Reliability Analysis through Survival Signature and Machine Learning Techniques | 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is network reliability analysis difficult for large-scale complex networks?","Question",{"text":75,"@type":76},"Network reliability estimation requires substantial computation because reliability calculation is NP-hard, making exhaustive or exact methods infeasible as networks grow in size and complexity.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed framework estimate survival signature and network reliability?",{"text":80,"@type":76},"It uses a graph-based neural network that aggregates feature information from neighboring nodes and further applies higher-order graph neural networks to capture both flow characteristics and topology information.",{"name":82,"@type":73,"acceptedAnswer":83},"What advantage does the graph-based neural network provide compared with traditional machine learning approaches?",{"text":84,"@type":76},"It integrates response flow characteristics and network topology, enabling highly accurate reliability estimates and allowing 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