{"title":"利用邻域信息改进平均场网络渗流模型","authors":"Chris Jones, Karoline Wiesner","doi":"10.1093/comnet/cnad029","DOIUrl":null,"url":null,"abstract":"Abstract Mean field theory models of percolation on networks provide analytic estimates of network robustness under node or edge removal. We introduce a new mean field theory model based on generating functions that includes information about the tree-likeness of each node’s local neighbourhood. We show that our new model outperforms all other generating function models in prediction accuracy when testing their estimates on a wide range of real-world network data. We compare the new model’s performance against the recently introduced message-passing models and provide evidence that the standard version is also outperformed, while the ‘loopy’ version is only outperformed on a targeted attack strategy. As we show, however, the computational complexity of our model implementation is much lower than that of message-passing algorithms. We provide evidence that all discussed models are poor in predicting networks with highly modular structure with dispersed modules, which are also characterized by high mixing times, identifying this as a general limitation of percolation prediction models.","PeriodicalId":15442,"journal":{"name":"Journal of complex networks","volume":"4 1","pages":"0"},"PeriodicalIF":2.2000,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving mean-field network percolation models with neighbourhood information\",\"authors\":\"Chris Jones, Karoline Wiesner\",\"doi\":\"10.1093/comnet/cnad029\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Mean field theory models of percolation on networks provide analytic estimates of network robustness under node or edge removal. We introduce a new mean field theory model based on generating functions that includes information about the tree-likeness of each node’s local neighbourhood. We show that our new model outperforms all other generating function models in prediction accuracy when testing their estimates on a wide range of real-world network data. We compare the new model’s performance against the recently introduced message-passing models and provide evidence that the standard version is also outperformed, while the ‘loopy’ version is only outperformed on a targeted attack strategy. As we show, however, the computational complexity of our model implementation is much lower than that of message-passing algorithms. We provide evidence that all discussed models are poor in predicting networks with highly modular structure with dispersed modules, which are also characterized by high mixing times, identifying this as a general limitation of percolation prediction models.\",\"PeriodicalId\":15442,\"journal\":{\"name\":\"Journal of complex networks\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2023-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of complex networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/comnet/cnad029\",\"RegionNum\":4,\"RegionCategory\":\"数学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of complex networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/comnet/cnad029","RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATHEMATICS, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Improving mean-field network percolation models with neighbourhood information
Abstract Mean field theory models of percolation on networks provide analytic estimates of network robustness under node or edge removal. We introduce a new mean field theory model based on generating functions that includes information about the tree-likeness of each node’s local neighbourhood. We show that our new model outperforms all other generating function models in prediction accuracy when testing their estimates on a wide range of real-world network data. We compare the new model’s performance against the recently introduced message-passing models and provide evidence that the standard version is also outperformed, while the ‘loopy’ version is only outperformed on a targeted attack strategy. As we show, however, the computational complexity of our model implementation is much lower than that of message-passing algorithms. We provide evidence that all discussed models are poor in predicting networks with highly modular structure with dispersed modules, which are also characterized by high mixing times, identifying this as a general limitation of percolation prediction models.
期刊介绍:
Journal of Complex Networks publishes original articles and reviews with a significant contribution to the analysis and understanding of complex networks and its applications in diverse fields. Complex networks are loosely defined as networks with nontrivial topology and dynamics, which appear as the skeletons of complex systems in the real-world. The journal covers everything from the basic mathematical, physical and computational principles needed for studying complex networks to their applications leading to predictive models in molecular, biological, ecological, informational, engineering, social, technological and other systems. It includes, but is not limited to, the following topics: - Mathematical and numerical analysis of networks - Network theory and computer sciences - Structural analysis of networks - Dynamics on networks - Physical models on networks - Networks and epidemiology - Social, socio-economic and political networks - Ecological networks - Technological and infrastructural networks - Brain and tissue networks - Biological and molecular networks - Spatial networks - Techno-social networks i.e. online social networks, social networking sites, social media - Other applications of networks - Evolving networks - Multilayer networks - Game theory on networks - Biomedicine related networks - Animal social networks - Climate networks - Cognitive, language and informational network