L. Lu, and T. Zhou. (2010)cite arxiv:1010.0725Comment: 44 pages, 5 figures.
Abstract
Link prediction in complex networks has attracted increasing attention from
both physical and computer science communities. The algorithms can be used to
extract missing information, identify spurious interactions, evaluate network
evolving mechanisms, and so on. This article summaries recent progress about
link prediction algorithms, emphasizing on the contributions from physical
perspectives and approaches, such as the random-walk-based methods and the
maximum likelihood methods. We also introduce three typical applications:
reconstruction of networks, evaluation of network evolving mechanism and
classification of partially labelled networks. Finally, we introduce some
applications and outline future challenges of link prediction algorithms.
%0 Generic
%1 lu2010prediction
%A Lu, Linyuan
%A Zhou, Tao
%D 2010
%K complex link networks prediction survey
%T Link Prediction in Complex Networks: A Survey
%U http://arxiv.org/abs/1010.0725
%X Link prediction in complex networks has attracted increasing attention from
both physical and computer science communities. The algorithms can be used to
extract missing information, identify spurious interactions, evaluate network
evolving mechanisms, and so on. This article summaries recent progress about
link prediction algorithms, emphasizing on the contributions from physical
perspectives and approaches, such as the random-walk-based methods and the
maximum likelihood methods. We also introduce three typical applications:
reconstruction of networks, evaluation of network evolving mechanism and
classification of partially labelled networks. Finally, we introduce some
applications and outline future challenges of link prediction algorithms.
@misc{lu2010prediction,
abstract = {Link prediction in complex networks has attracted increasing attention from
both physical and computer science communities. The algorithms can be used to
extract missing information, identify spurious interactions, evaluate network
evolving mechanisms, and so on. This article summaries recent progress about
link prediction algorithms, emphasizing on the contributions from physical
perspectives and approaches, such as the random-walk-based methods and the
maximum likelihood methods. We also introduce three typical applications:
reconstruction of networks, evaluation of network evolving mechanism and
classification of partially labelled networks. Finally, we introduce some
applications and outline future challenges of link prediction algorithms.},
added-at = {2014-06-17T03:41:41.000+0200},
author = {Lu, Linyuan and Zhou, Tao},
biburl = {https://www.bibsonomy.org/bibtex/23c168b71c855364c1a554ad493b5aaf5/lbalby},
description = {Link Prediction in Complex Networks: A Survey},
interhash = {7f1a97dd145f18d2471a4fad5e0d8f4c},
intrahash = {3c168b71c855364c1a554ad493b5aaf5},
keywords = {complex link networks prediction survey},
note = {cite arxiv:1010.0725Comment: 44 pages, 5 figures},
timestamp = {2014-06-17T03:41:41.000+0200},
title = {Link Prediction in Complex Networks: A Survey},
url = {http://arxiv.org/abs/1010.0725},
year = 2010
}