کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7538356 | 1488847 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Change we can believe in: Comparing longitudinal network models on consistency, interpretability and predictive power
ترجمه فارسی عنوان
تغییر ما می توانیم باور داشته باشیم: مقایسه مدل های طولی شبکه با هماهنگی، تفسیر پذیری و قدرت پیش بینی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
ریاضیات
آمار و احتمال
چکیده انگلیسی
While several models for analysing longitudinal network data have been proposed, their main differences, especially regarding the treatment of time, have not been discussed extensively in the literature. However, differences in treatment of time strongly impact the conclusions that can be drawn from data. In this article we compare auto-regressive network models using the example of TERGMs - a temporal extensions of ERGMs - and process-based models using SAOMs as an example. We conclude that the TERGM has, in contrast to the ERGM, no consistent interpretation on tie-level probabilities, as well as no consistent interpretation on processes of network change. Further, parameters in the TERGM are strongly dependent on the interval length between two time-points. Neither limitation is true for process-based network models such as the SAOM. Finally, both compared models perform poorly in out-of-sample prediction compared to trivial predictive models.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Social Networks - Volume 52, January 2018, Pages 180-191
Journal: Social Networks - Volume 52, January 2018, Pages 180-191
نویسندگان
Per Block, Johan Koskinen, James Hollway, Christian Steglich, Christoph Stadtfeld,