{"title":"时间指数随机图模型和面向随机参与者模型的理论和经验比较——勘误表","authors":"Philip Leifeld, S. Cranmer","doi":"10.1017/nws.2022.11","DOIUrl":null,"url":null,"abstract":"Block, P., Hollway, J., Stadtfeld, C., Koskinen, J., & Snijders, T. (2022). Circular specifications and “predicting” with information from the future: Errors in the empirical SAOM–TERGM comparison of Leifeld & Cranmer. Network Science, 10(1). https://doi.org/10.1017/nws.2022.6 Leifeld, P., & Cranmer, S. (2019a). A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model. Network Science, 7(1), 20–51. https://doi.org/10.1017/nws.2018.26 Leifeld, P., & Cranmer, S. (2019b). Replication Data for: A Theoretical and Empirical Comparison of the Temporal Exponential Random Graph Model and the Stochastic Actor-Oriented Model, https://doi.org/10.7910/DVN/NEM2XU, Harvard Dataverse, V1. Leifeld, P., & Cranmer, S. (2022). The stochastic actor-oriented model is a theory as much as it is a method and must be subject to theory tests. Network Science, 10(1). https://doi.org/10.1017/nws.2022.7 Wasserman, S., & Brandes, U. (2022) Editors’ Note. Network Science, 10(1). https://doi.org/10.1017/nws.2022.8","PeriodicalId":51827,"journal":{"name":"Network Science","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2022-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model – Corrigendum\",\"authors\":\"Philip Leifeld, S. Cranmer\",\"doi\":\"10.1017/nws.2022.11\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Block, P., Hollway, J., Stadtfeld, C., Koskinen, J., & Snijders, T. (2022). Circular specifications and “predicting” with information from the future: Errors in the empirical SAOM–TERGM comparison of Leifeld & Cranmer. Network Science, 10(1). https://doi.org/10.1017/nws.2022.6 Leifeld, P., & Cranmer, S. (2019a). A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model. Network Science, 7(1), 20–51. https://doi.org/10.1017/nws.2018.26 Leifeld, P., & Cranmer, S. (2019b). Replication Data for: A Theoretical and Empirical Comparison of the Temporal Exponential Random Graph Model and the Stochastic Actor-Oriented Model, https://doi.org/10.7910/DVN/NEM2XU, Harvard Dataverse, V1. Leifeld, P., & Cranmer, S. (2022). The stochastic actor-oriented model is a theory as much as it is a method and must be subject to theory tests. Network Science, 10(1). https://doi.org/10.1017/nws.2022.7 Wasserman, S., & Brandes, U. (2022) Editors’ Note. Network Science, 10(1). https://doi.org/10.1017/nws.2022.8\",\"PeriodicalId\":51827,\"journal\":{\"name\":\"Network Science\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2022-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Network Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1017/nws.2022.11\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"SOCIAL SCIENCES, INTERDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Network Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/nws.2022.11","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"SOCIAL SCIENCES, INTERDISCIPLINARY","Score":null,"Total":0}
A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model – Corrigendum
Block, P., Hollway, J., Stadtfeld, C., Koskinen, J., & Snijders, T. (2022). Circular specifications and “predicting” with information from the future: Errors in the empirical SAOM–TERGM comparison of Leifeld & Cranmer. Network Science, 10(1). https://doi.org/10.1017/nws.2022.6 Leifeld, P., & Cranmer, S. (2019a). A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model. Network Science, 7(1), 20–51. https://doi.org/10.1017/nws.2018.26 Leifeld, P., & Cranmer, S. (2019b). Replication Data for: A Theoretical and Empirical Comparison of the Temporal Exponential Random Graph Model and the Stochastic Actor-Oriented Model, https://doi.org/10.7910/DVN/NEM2XU, Harvard Dataverse, V1. Leifeld, P., & Cranmer, S. (2022). The stochastic actor-oriented model is a theory as much as it is a method and must be subject to theory tests. Network Science, 10(1). https://doi.org/10.1017/nws.2022.7 Wasserman, S., & Brandes, U. (2022) Editors’ Note. Network Science, 10(1). https://doi.org/10.1017/nws.2022.8
期刊介绍：
Network Science is an important journal for an important discipline - one using the network paradigm, focusing on actors and relational linkages, to inform research, methodology, and applications from many fields across the natural, social, engineering and informational sciences. Given growing understanding of the interconnectedness and globalization of the world, network methods are an increasingly recognized way to research aspects of modern society along with the individuals, organizations, and other actors within it. The discipline is ready for a comprehensive journal, open to papers from all relevant areas. Network Science is a defining work, shaping this discipline. The journal welcomes contributions from researchers in all areas working on network theory, methods, and data.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
