Message-passing approach to higher-order percolation

作者全名："Peng, Hao; Qian, Cheng; Zhao, Dandan; Zhong, Ming; Han, Jianmin; Zhou, Tao; Wang, Wei"

作者地址："[Peng, Hao] Zhejiang Normal Univ, Key Lab Intelligent Educ Technol & Applicat Zhejia, Jinhua 321004, Peoples R China; [Peng, Hao; Qian, Cheng; Zhao, Dandan; Zhong, Ming; Han, Jianmin] Zhejiang Normal Univ, Sch Comp Sci & Technol, Jinhua 321004, Peoples R China; [Zhou, Tao] Univ Elect Sci & Technol China, Big Data Res Ctr, Chengdu 610054, Peoples R China; [Zhou, Tao] Univ Elect Sci & Technol China, Complex Lab, Chengdu 610054, Peoples R China; [Wang, Wei] Chongqing Med Univ, Sch Publ Hlth, Chongqing 400016, Peoples R China"

通信作者："Wang, W (通讯作者)，Chongqing Med Univ, Sch Publ Hlth, Chongqing 400016, Peoples R China."

来源：PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS

ESI学科分类：PHYSICS

WOS号：WOS:001142664200001

JCR分区：Q1

影响因子：3.3

年份：2024

卷号：634

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：Hypergraph; Network robustness; Higher-order percolation; Message-passing approach

摘要："Hypergraph describes real-world networks widely because it captures pairwise and multiple nodes' interactions. Various kinds of damages, such as network attacks, hardware malfunctions, and communication disruptions, may impair the function of those real-world systems. We propose a generalized higher-order percolation model to investigate the robustness of the hypergraph, in which the nodes and hyperedges were randomly removed with probabilities. An accurate approach to studying the higher-order percolation model should overcome non-local tree-like structures and higher-order interactions, which makes the classical mean-field approach invalid. To this end, we develop a message-passing approach in which we first transform the hypergraph into a factor graph then develop a message-passing approach on the factor graph. Through extensive experimental studies on both artificial and real-world hypergraphs, our theory can accurately predict numerical results. The experimental data demonstrate that our theory achieves average accuracy rates in calculating giant connected component (GCC) size of 99.87% for artificial loopless hypergraphs, 99.24% for artificial hypergraphs with loops, and 99.65% for real-world hypergraphs. Our findings provide another way to understand the robustness of hypergraphs, and also provide certain ideas for studying complex systems in various fields."

基金机构：　

基金资助正文：