{"title":"反常随机网络","authors":"Hong Zhang, Guohua Li","doi":"arxiv-2406.18882","DOIUrl":null,"url":null,"abstract":"After the groundbreaking work of Erd$\\ddot{o}$s-R$\\acute{e}$nyi random graph,\nthe random networks has made great progress in recent years. One of the\neye-catching modeling is time-varying random network model capable of encoding\nthe instantaneous time description of the network dynamics. To further describe\nthe random duration time for the nodes to be inactive, we herein propose a\ndinner party anomalous random networks model, and derive the analytical\nsolution of the probability density function for the node being active at a\ngiven time. Moreover, we investigate the gift delivery and viral transmission\nin dinner party random networks. This work provides new quantitative insights\nin describing random networks, and could help model other uncertainty phenomena\nin real networks.","PeriodicalId":501066,"journal":{"name":"arXiv - PHYS - Disordered Systems and Neural Networks","volume":"17 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Anomalous random networks\",\"authors\":\"Hong Zhang, Guohua Li\",\"doi\":\"arxiv-2406.18882\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"After the groundbreaking work of Erd$\\\\ddot{o}$s-R$\\\\acute{e}$nyi random graph,\\nthe random networks has made great progress in recent years. One of the\\neye-catching modeling is time-varying random network model capable of encoding\\nthe instantaneous time description of the network dynamics. To further describe\\nthe random duration time for the nodes to be inactive, we herein propose a\\ndinner party anomalous random networks model, and derive the analytical\\nsolution of the probability density function for the node being active at a\\ngiven time. Moreover, we investigate the gift delivery and viral transmission\\nin dinner party random networks. This work provides new quantitative insights\\nin describing random networks, and could help model other uncertainty phenomena\\nin real networks.\",\"PeriodicalId\":501066,\"journal\":{\"name\":\"arXiv - PHYS - Disordered Systems and Neural Networks\",\"volume\":\"17 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - PHYS - Disordered Systems and Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2406.18882\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Disordered Systems and Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2406.18882","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
After the groundbreaking work of Erd$\ddot{o}$s-R$\acute{e}$nyi random graph,
the random networks has made great progress in recent years. One of the
eye-catching modeling is time-varying random network model capable of encoding
the instantaneous time description of the network dynamics. To further describe
the random duration time for the nodes to be inactive, we herein propose a
dinner party anomalous random networks model, and derive the analytical
solution of the probability density function for the node being active at a
given time. Moreover, we investigate the gift delivery and viral transmission
in dinner party random networks. This work provides new quantitative insights
in describing random networks, and could help model other uncertainty phenomena
in real networks.