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  5. Simmelian Backbones: Amplifying Hidden Homophily in Facebook Networks
 
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Simmelian Backbones: Amplifying Hidden Homophily in Facebook Networks

Author(s)
Nick, Bobo  
Lee, Conrad  
Cunningham, Pádraig  
Brandes, Ulrik  
Uri
http://hdl.handle.net/10197/7544
Date Issued
2013-08-28
Date Available
2016-04-04T11:51:51Z
Abstract
Empirical social networks are often aggregate proxies for several heterogeneous relations. In online social networks, for instance, interactions related to friendship, kinship, business, interests, and other relationships may all be represented as catchall 'friendships.' Because several relations are mingled into one, the resulting networks exhibit relatively high and uniform density. As a consequence, the variation in positional differences and local cohesion may be too small for reliable analysis. We introduce a method to identify the essential relationships in networks representing social interactions. Our method is based on a novel concept of triadic cohesion that is motivated by Simmel's concept of membership in social groups. We demonstrate that our Simmelian backbones are capable of extracting structure from Facebook interaction networks that makes them easy to visualize and analyze. Since all computations are local, the method can be restricted to partial networks such as ego networks, and scales to big data.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Deutsche Forschungsgemeinschaft
University of Konstanz
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2013 ACM
Subjects

Machine learning

Statistics

DOI
10.1145/2492517.2492569
Language
English
Status of Item
Peer reviewed
Journal
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM '13)
Conference Details
2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 25-28 August 2013, Niagara Falls, Ontario, Canada
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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insight_publication.pdf

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7.6 MB

Format

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Checksum (MD5)

70847921b93201b7338047c0416dea79

Owning collection
Insight Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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