D. Liben-Nowell, and J. Kleinberg. 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 <i>link prediction problem</i>, 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.
Description
The link prediction problem for social networks based on snapshot data (no time series)
%0 Conference Paper
%1 Liben-Nowell:2003:LPP:956863.956972
%A Liben-Nowell, David
%A Kleinberg, Jon
%B Proceedings of the twelfth international conference on Information and knowledge management
%C New York, NY, USA
%D 2003
%I ACM
%K LinkPrediction RelatedWork
%P 556--559
%R 10.1145/956863.956972
%T The link prediction problem for social networks
%U http://doi.acm.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 <i>link prediction problem</i>, 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{Liben-Nowell:2003:LPP: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 <i>link prediction problem</i>, 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.},
acmid = {956972},
added-at = {2013-04-07T20:38:56.000+0200},
address = {New York, NY, USA},
author = {Liben-Nowell, David and Kleinberg, Jon},
biburl = {https://www.bibsonomy.org/bibtex/2a4009a71855e6625101cc49bafd24298/macek},
booktitle = {Proceedings of the twelfth international conference on Information and knowledge management},
description = {The link prediction problem for social networks based on snapshot data (no time series)},
doi = {10.1145/956863.956972},
interhash = {1920da9fec5905f031a0ab3919553362},
intrahash = {a4009a71855e6625101cc49bafd24298},
isbn = {1-58113-723-0},
keywords = {LinkPrediction RelatedWork},
location = {New Orleans, LA, USA},
numpages = {4},
pages = {556--559},
publisher = {ACM},
series = {CIKM '03},
timestamp = {2013-04-07T20:38:57.000+0200},
title = {The link prediction problem for social networks},
url = {http://doi.acm.org/10.1145/956863.956972},
year = 2003
}