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Using semidiscrete decomposition and vector space models to identify users of social networks
Author(s) -
Gosnell Denise K.,
Berry Michael W.
Publication year - 2015
Publication title -
numerical linear algebra with applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.02
H-Index - 53
eISSN - 1099-1506
pISSN - 1070-5325
DOI - 10.1002/nla.1989
Subject(s) - social network (sociolinguistics) , computer science , identification (biology) , adjacency list , focus (optics) , social network analysis , uniqueness , decomposition , adjacency matrix , fingerprint (computing) , space (punctuation) , social media , data mining , data science , world wide web , artificial intelligence , theoretical computer science , mathematics , algorithm , physics , ecology , graph , botany , optics , biology , mathematical analysis , operating system
Summary Users of social media interact with the network and its users. Each interaction creates network‐specific data between the engaged users and the chosen social avenue. Over time, these engagements accumulate to describe the user's social fingerprint, a data trail that encapsulates the user's identity on the network. The agglomeration of this information showcases the user's activity on the social network and establishes a traceable social fingerprint. The focus of this study is to examine the accuracy of user identification via semidiscrete decomposition of social network adjacency matrices. The use of semidiscrete decomposition in this application creates separability amongst observed data trails as a means to establish uniqueness amongst the users of the social network. The results presented herein illustrate to what extent a user's social fingerprint can be both quantified and identified on a social network through time. Copyright © 2015 John Wiley & Sons, Ltd.