{"title":"指数随机图建模的最新进展","authors":"A. Caimo, Isabella Gollini","doi":"10.1353/mpr.2023.0000","DOIUrl":null,"url":null,"abstract":"Abstract:Exponential random graph models (ERGMs) are one of the most popular statistical methods for analysing relational network structures. ERGMs represent generative statistical network processes that allow researchers to specify sufficient statistics in the form of counts of network configurations associated to potential dependencies between and across particular sets of nodes. In this paper, we review some of the most interesting recent advances for the ERGM framework. In particular, we focus on the modelling extensions for valued, multi-layer and multi-level networks.","PeriodicalId":434988,"journal":{"name":"Mathematical Proceedings of the Royal Irish Academy","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Recent Advances In Exponential Random Graph Modelling\",\"authors\":\"A. Caimo, Isabella Gollini\",\"doi\":\"10.1353/mpr.2023.0000\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract:Exponential random graph models (ERGMs) are one of the most popular statistical methods for analysing relational network structures. ERGMs represent generative statistical network processes that allow researchers to specify sufficient statistics in the form of counts of network configurations associated to potential dependencies between and across particular sets of nodes. In this paper, we review some of the most interesting recent advances for the ERGM framework. In particular, we focus on the modelling extensions for valued, multi-layer and multi-level networks.\",\"PeriodicalId\":434988,\"journal\":{\"name\":\"Mathematical Proceedings of the Royal Irish Academy\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mathematical Proceedings of the Royal Irish Academy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1353/mpr.2023.0000\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mathematical Proceedings of the Royal Irish Academy","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1353/mpr.2023.0000","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recent Advances In Exponential Random Graph Modelling
Abstract:Exponential random graph models (ERGMs) are one of the most popular statistical methods for analysing relational network structures. ERGMs represent generative statistical network processes that allow researchers to specify sufficient statistics in the form of counts of network configurations associated to potential dependencies between and across particular sets of nodes. In this paper, we review some of the most interesting recent advances for the ERGM framework. In particular, we focus on the modelling extensions for valued, multi-layer and multi-level networks.
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