Origins of power-law degree distribution in the heterogeneity of human activity in social networks

Lev Muchnik, Sen Pei, Lucas C. Parra, Saulo D.S. Reis, José S. Andrade, Shlomo Havlin, Hernán A. Makse*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

148 Scopus citations

Abstract

The probability distribution of number of ties of an individual in a social network follows a scale-free power-law. However, how this distribution arises has not been conclusively demonstrated in direct analyses of people's actions in social networks. Here, we perform a causal inference analysis and find an underlying cause for this phenomenon. Our analysis indicates that heavy-tailed degree distribution is causally determined by similarly skewed distribution of human activity. Specifically, the degree of an individual is entirely random - following a "maximum entropy attachment" model - except for its mean value which depends deterministically on the volume of the users' activity. This relation cannot be explained by interactive models, like preferential attachment, since the observed actions are not likely to be caused by interactions with other people.

Original languageAmerican English
Article number1783
JournalScientific Reports
Volume3
DOIs
StatePublished - 2013

Bibliographical note

Funding Information:
“We thank G. Khazankin, Research Institute of Physiology SB RAMS for kindly providing access to invaluable data on news2.ru user activity. The research is supported by NSF Emerging Frontiers, ARL, FP7 project SOCIONICAL and MULTIPLEX, CNPq, CAPES, FUNCAP and NSFC (11290141, 11201018).”

Funding Information:
“We thank G. Khazankin, Research Institute of Physiology SB RAMS for kindly providing access to invaluable data on news2.ru user activity. The research is supported by NSF Emerging Frontiers, Army Research Laboratory Cooperative Agreement Number W911NF-09-2-0053 (the ARL Network Science CTA), FP7 project SOCIONICAL and MULTIPLEX, CNPq, CAPES, FUNCAP and NSFC (11290141, 11201018).” This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/ licenses/by-nc-nd/4.0/

Funding Information:
We thank G. Khazankin, Research Institute of Physiology SB RAMS for kindly providing access to invaluable data on news2.ru user activity. The research is supported by NSF Emerging Frontiers, ARL, FP7 project SOCIONICAL and MULTIPLEX, CNPq, CAPES, FUNCAP and NSFC (11290141, 11201018).

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