Propinquity drives the emergence of network structure and density

Lazaros K. Gallos (), Shlomo Havlin, H. Eugene Stanley () and Nina H. Fefferman
Additional contact information
Lazaros K. Gallos: Center for Discrete Mathematics and Theoretical Computer Science (DIMACS), Rutgers University, Piscataway, NJ 08854
Shlomo Havlin: Department of Physics, Bar-Ilan University, Ramat Gan 52900, Israel
H. Eugene Stanley: Physics Department, Boston University, Boston, MA 02215; Center for Polymer Studies, Boston University, Boston, MA 02215
Nina H. Fefferman: Department of Mathematics, University of Tennessee, Knoxville, TN 37996; Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN 37996

Proceedings of the National Academy of Sciences, 2019, vol. 116, issue 41, 20360-20365

Abstract: The lack of large-scale, continuously evolving empirical data usually limits the study of networks to the analysis of snapshots in time. This approach has been used for verification of network evolution mechanisms, such as preferential attachment. However, these studies are mostly restricted to the analysis of the first links established by a new node in the network and typically ignore connections made after each node’s initial introduction. Here, we show that the subsequent actions of individuals, such as their second network link, are not random and can be decoupled from the mechanism behind the first network link. We show that this feature has strong influence on the network topology. Moreover, snapshots in time can now provide information on the mechanism used to establish the second connection. We interpret these empirical results by introducing the “propinquity model,” in which we control and vary the distance of the second link established by a new node and find that this can lead to networks with tunable density scaling, as found in real networks. Our work shows that sociologically meaningful mechanisms are influencing network evolution and provides indications of the importance of measuring the distance between successive connections.

Keywords: network generation methods; network density; network evolution (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.pnas.org/content/116/41/20360.full (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nas:journl:v:116:y:2019:p:20360-20365

Access Statistics for this article

More articles in Proceedings of the National Academy of Sciences from Proceedings of the National Academy of Sciences
Bibliographic data for series maintained by PNAS Product Team ().