动态社会网络中记忆衰减建模的贝叶斯半参数方法

IF 6.5 2区 社会学 Q1 SOCIAL SCIENCES, MATHEMATICAL METHODS Sociological Methods & Research Pub Date : 2022-08-15 DOI:10.1177/00491241221113875
Giuseppe Arena, Joris Mulder, Roger Th. A.J. Leenders
{"title":"动态社会网络中记忆衰减建模的贝叶斯半参数方法","authors":"Giuseppe Arena, Joris Mulder, Roger Th. A.J. Leenders","doi":"10.1177/00491241221113875","DOIUrl":null,"url":null,"abstract":"In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the volume of past interactio...","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"49 15","pages":""},"PeriodicalIF":6.5000,"publicationDate":"2022-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A Bayesian Semi-Parametric Approach for Modeling Memory Decay in Dynamic Social Networks\",\"authors\":\"Giuseppe Arena, Joris Mulder, Roger Th. A.J. Leenders\",\"doi\":\"10.1177/00491241221113875\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the volume of past interactio...\",\"PeriodicalId\":21849,\"journal\":{\"name\":\"Sociological Methods & Research\",\"volume\":\"49 15\",\"pages\":\"\"},\"PeriodicalIF\":6.5000,\"publicationDate\":\"2022-08-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sociological Methods & Research\",\"FirstCategoryId\":\"90\",\"ListUrlMain\":\"https://doi.org/10.1177/00491241221113875\",\"RegionNum\":2,\"RegionCategory\":\"社会学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"SOCIAL SCIENCES, MATHEMATICAL METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sociological Methods & Research","FirstCategoryId":"90","ListUrlMain":"https://doi.org/10.1177/00491241221113875","RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"SOCIAL SCIENCES, MATHEMATICAL METHODS","Score":null,"Total":0}
引用次数: 5

摘要

在关系事件网络中,行为者相互互动的倾向在很大程度上取决于社会网络中行为者之间过去的互动。过去的互动量……
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
A Bayesian Semi-Parametric Approach for Modeling Memory Decay in Dynamic Social Networks
In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the volume of past interactio...
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
16.30
自引率
3.20%
发文量
40
期刊介绍: Sociological Methods & Research is a quarterly journal devoted to sociology as a cumulative empirical science. The objectives of SMR are multiple, but emphasis is placed on articles that advance the understanding of the field through systematic presentations that clarify methodological problems and assist in ordering the known facts in an area. Review articles will be published, particularly those that emphasize a critical analysis of the status of the arts, but original presentations that are broadly based and provide new research will also be published. Intrinsically, SMR is viewed as substantive journal but one that is highly focused on the assessment of the scientific status of sociology. The scope is broad and flexible, and authors are invited to correspond with the editors about the appropriateness of their articles.
期刊最新文献
Sharing Big Video Data: Ethics, Methods, and Technology Dynamics of Health Expectancy: An Introduction to the Multiple Multistate Method (MMM) Seeded Topic Models in Digital Archives: Analyzing Interpretations of Immigration in Swedish Newspapers, 1945–2019 A Primer on Deep Learning for Causal Inference Untapped Potential: Designed Digital Trace Data in Online Survey Experiments
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1