Illustration of the dyadwise shared partner endogenous variable with <i>k</i> = 1, <i>k</i> = 2 and <i>k</i> = 3 respectively.

Illustration of the dyadwise shared partner endogenous variable with <i>k</i> = 1, <i>k</i> = 2 and <i>k</i> = 3 respectively.

<p>(Source: The original graph appeared as Fig 11 in [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0314557#pone.0314557.ref026" target="_blank">26</a>]).</p>

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Bibliographic Details
Main Author:	Helal El-Zaatari (20442243) (author)
Other Authors:	Fei Yu (61577) (author), Michael R. Kosorok (7538252) (author)
Published:	2024
Subjects:	Neuroscience Biotechnology Cancer Infectious Diseases Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified wise feature selection providing meaningful insights endogenous variable selection approach effectively manages addressing critical challenges degenerate network models complex network interactions accommodating network dependencies method &# 8217 interpreting social networks ergms )</ p undirected networks method integrates network analysis xlink "> traditional ergms systematic step study introduces stochastic step robust framework particularly notable nine real future advancements findings contribute ergm degeneracy empirical application computational complexity
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