140
Views
1
CrossRef citations to date
0
Altmetric
Research Article

A dynamic process reference model for sparse networks with reciprocity

Pages 1-27 | Received 30 Dec 2019, Accepted 11 Jul 2020, Published online: 25 Jul 2020
 

ABSTRACT

Many social and other networks exhibit stable size scaling relationships, such that features such as mean degree or reciprocation rates change slowly or are approximately constant as the number of vertices increases. Statistical network models built on top of simple Bernoulli baseline (or reference) measures often behave unrealistically in this respect, leading to the development of sparse reference models that preserve features such as mean degree scaling. In this paper, we generalize recent work on the micro-foundations of such reference models to the case of sparse directed graphs with non-vanishing reciprocity, providing a dynamic process interpretation of the emergence of stable macroscopic behavior.

Notes

1 Contrary to what the name implies, networks undergoing “power law densification” are actually growing more sparse. They are, however, doing so more slowly than they would under constant mean degree.

Additional information

Funding

This work was supported by NSF awards SES [1826589], IIS [1939237], and DMS [1361425].

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.