{"title":"考虑迁移行为的加权网络演化","authors":"Yihong Hu, Daoli Zhu, Nianqu Zhu","doi":"10.1109/ICIII.2008.288","DOIUrl":null,"url":null,"abstract":"This paper presents a general model of weighted networks evolution in which the structural growth and weight dynamics are both induced by transfer behavior which is an obvious and important phenomenon in technological networks and social networks, e.g. passengers often transfer from a third airport instead of flying directly to the destination in airline networks. In this model we assume at each time step there emerges a new node with m destinations. The new node either connects the destination directly with the probability p or transfers from a third node with the probability 1-p. The analytical result shows degree, weight and strength all obey power-law distribution with the exponent depending on the parameter p. The clustering coefficient and degree assortatively coefficient are high under small p indicating transfer behavior contributes to clusters formation and hierarchal structures.","PeriodicalId":185591,"journal":{"name":"2008 International Conference on Information Management, Innovation Management and Industrial Engineering","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Weighted Network Evolution with Transfer Behavior\",\"authors\":\"Yihong Hu, Daoli Zhu, Nianqu Zhu\",\"doi\":\"10.1109/ICIII.2008.288\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a general model of weighted networks evolution in which the structural growth and weight dynamics are both induced by transfer behavior which is an obvious and important phenomenon in technological networks and social networks, e.g. passengers often transfer from a third airport instead of flying directly to the destination in airline networks. In this model we assume at each time step there emerges a new node with m destinations. The new node either connects the destination directly with the probability p or transfers from a third node with the probability 1-p. The analytical result shows degree, weight and strength all obey power-law distribution with the exponent depending on the parameter p. The clustering coefficient and degree assortatively coefficient are high under small p indicating transfer behavior contributes to clusters formation and hierarchal structures.\",\"PeriodicalId\":185591,\"journal\":{\"name\":\"2008 International Conference on Information Management, Innovation Management and Industrial Engineering\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-12-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2008 International Conference on Information Management, Innovation Management and Industrial Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIII.2008.288\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 International Conference on Information Management, Innovation Management and Industrial Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIII.2008.288","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
This paper presents a general model of weighted networks evolution in which the structural growth and weight dynamics are both induced by transfer behavior which is an obvious and important phenomenon in technological networks and social networks, e.g. passengers often transfer from a third airport instead of flying directly to the destination in airline networks. In this model we assume at each time step there emerges a new node with m destinations. The new node either connects the destination directly with the probability p or transfers from a third node with the probability 1-p. The analytical result shows degree, weight and strength all obey power-law distribution with the exponent depending on the parameter p. The clustering coefficient and degree assortatively coefficient are high under small p indicating transfer behavior contributes to clusters formation and hierarchal structures.