对“关于妄想和其他社交网络中测地周期长度分布的评论”的回复

Q2 Social Sciences
A. Stivala
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引用次数: 2

摘要

摘要Martin(2020)在我的贡献(Stivala 2020)中描述了对指数随机图(ERGM)参数的误解,使用该参数模型掩盖而不是阐明了数据。他认为,这是社交网络社区朝着专注于ERGM使用的方法单一文化发展的趋势的症状。在这篇回复中,我试图澄清这种情况是如何在这个特定的案例中出现的,并解决Martin提出的一些更普遍的问题,包括节点协变量的使用,我们可以从ERGM中学到什么,以及社交网络研究中的方法论单一文化主义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reply to “Comment on Geodesic Cycle Length Distributions in Delusional and Other Social Networks”
Abstract Martin (2020) describes a misinterpretation of exponential random graph (ERGM) parameters in my contribution (Stivala 2020), with the use of this parametric model obscuring, rather than illuminating, the data. He suggests that this is symptomatic of a trend in the social networks community towards a methodological monoculture focussed on the use of ERGMs. In this Reply I try to clarify how this situation arose in this specific case, and address some more general issues Martin raises, including the use of nodal covariates, what we can learn from ERGMs, and methodological monoculturalism in social network research.
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来源期刊
Journal of Social Structure
Journal of Social Structure Social Sciences-Sociology and Political Science
CiteScore
1.30
自引率
0.00%
发文量
0
审稿时长
24 weeks
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