Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/115309
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Type: Conference paper
Title: Which friends are more popular than you? Contact strength and the friendship paradox in social networks
Author: Bagrow, J.
Danforth, C.
Mitchell, L.
Citation: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2017 / pp.103-108
Publisher: ACM
Publisher Place: online
Issue Date: 2017
ISBN: 978-1-4503-4993-2
Conference Name: 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM '17) (31 Jul 2017 - 03 Aug 2017 : Sydney, Australia)
Statement of
Responsibility: 
James P. Bagrow, Christopher M. Danforth and Lewis Mitchell
Abstract: The friendship paradox states that in a social network, egos tend to have lower degree than their alters, or, “your friends have more friends than you do”. Most research has focused on the friendship paradox and its implications for information transmission, but treating the network as static and unweighted. Yet, people can dedicate only a finite fraction of their attention budget to each social interaction: a high-degree individual may have less time to dedicate to individual social links, forcing them to modulate the quantities of contact made to their different social ties. Here we study the friendship paradox in the context of differing contact volumes between egos and alters, finding a connection between contact volume and the strength of the friendship paradox. The most frequently contacted alters exhibit a less pronounced friendship paradox compared with the ego, whereas less-frequently contacted alters are more likely to be high degree and give rise to the paradox. We argue therefore for a more nuanced version of the friendship paradox: “your closest friends have slightly more friends than you do”, and in certain networks even: “your best friend has no more friends than you do”. We demonstrate that this relationship is robust, holding in both a social media and a mobile phone dataset. These results have implications for information transfer and influence in social networks, which we explore using a simple dynamical model.
Rights: © 2017 Association for Computing Machinery
RMID: 0030080556
DOI: 10.1145/3110025.3110027
Appears in Collections:Mathematical Sciences publications

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