A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model – Corrigendum

IF 1.4 Q2 SOCIAL SCIENCES, INTERDISCIPLINARY

Network Science Pub Date : 2022-03-01 DOI:10.1017/nws.2022.11

Philip Leifeld, S. Cranmer

引用次数: 0

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

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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

Block，P.、Hollway，J.、Stadtfeld，C.、Koskinen，J.和Snijders，T.（2022）。循环规范和未来信息的“预测”：Leifeld&Cranmer的经验SAOM–TERGM比较中的错误。网络科学，10（1）。https://doi.org/10.1017/nws.2022.6Leifeld，P.和Cranmer，S.（2019a）。时间指数随机图模型和面向随机参与者模型的理论和实证比较。网络科学，7（1），20-51。https://doi.org/10.1017/nws.2018.26Leifeld，P.和Cranmer，S.（2019b）。复制数据：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，https://doi.org/10.7910/DVN/NEM2XU，Harvard Dataverse，V1。Leifeld，P.和Cranmer，S.（2022）。随机行动者导向模型既是一种理论，也是一种方法，必须经过理论检验。网络科学，10（1）。https://doi.org/10.1017/nws.2022.7Wasserman，S.和Brandes，U.（2022）编者按。网络科学，10（1）。https://doi.org/10.1017/nws.2022.8

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来源期刊

Network Science SOCIAL SCIENCES, INTERDISCIPLINARY-

CiteScore

3.50

自引率

5.90%

发文量

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.