Simmelian Backbones: Amplifying Hidden Homophily in Facebook Networks

Files in This Item:
File Description SizeFormat 
insight_publication.pdf7.78 MBAdobe PDFDownload
Title: Simmelian Backbones: Amplifying Hidden Homophily in Facebook Networks
Authors: Nick, Bobo
Lee, Conrad
Cunningham, Pádraig
Brandes, Ulrik
Permanent link:
Date: 28-Aug-2013
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.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: IEEE
Copyright (published version): 2013 ACM
Keywords: Machine learningStatistics
DOI: 10.1145/2492517.2492569
Language: en
Status of Item: Peer reviewed
Is part of: 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 2016-04-04T11:51:51Z
Appears in Collections:Insight Research Collection

Show full item record

Citations 20

Last Week
Last month
checked on Dec 12, 2018

Google ScholarTM



This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.