D. Liben-Nowell, and J. Kleinberg. CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management, page 556--559. New York, NY, USA, ACM, (2003)
DOI: 10.1145/956863.956972
Abstract
Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link prediction problem , and develop approaches to link prediction based on measures the "proximity" of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.
%0 Conference Paper
%1 liben2003link
%A Liben-Nowell, David
%A Kleinberg, Jon
%B CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management
%C New York, NY, USA
%D 2003
%I ACM
%K analysis network, similarity, social, web-graph,
%P 556--559
%R 10.1145/956863.956972
%T The link prediction problem for social networks
%U http://dx.doi.org/10.1145/956863.956972
%X Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link prediction problem , and develop approaches to link prediction based on measures the "proximity" of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.
%@ 1-58113-723-0
@inproceedings{liben2003link,
abstract = {Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link prediction problem , and develop approaches to link prediction based on measures the "proximity" of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.},
added-at = {2009-08-17T13:31:04.000+0200},
address = {New York, NY, USA},
author = {Liben-Nowell, David and Kleinberg, Jon},
biburl = {https://www.bibsonomy.org/bibtex/25ea4aec308221402c617efa9559e5ee5/schweste},
booktitle = {CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management},
citeulike-article-id = {595771},
citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=956972},
citeulike-linkout-1 = {http://dx.doi.org/10.1145/956863.956972},
citeulike-linkout-2 = {http://www.eecs.harvard.edu/\~{}michaelm/E210/linkpred.pdf},
doi = {10.1145/956863.956972},
interhash = {1920da9fec5905f031a0ab3919553362},
intrahash = {5ea4aec308221402c617efa9559e5ee5},
isbn = {1-58113-723-0},
keywords = {analysis network, similarity, social, web-graph,},
location = {New Orleans, LA, USA},
pages = {556--559},
posted-at = {2006-10-10 15:36:58},
priority = {0},
publisher = {ACM},
timestamp = {2009-08-17T13:31:04.000+0200},
title = {The link prediction problem for social networks},
url = {http://dx.doi.org/10.1145/956863.956972},
year = 2003
}