Keyword(s):OWA Operator, Link Prediction, Social Network Analysis, Ensemble.

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Ontology
Subjects/Areas/Topics:Ensemble Methods
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Pattern Recognition
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Theory and Methods

Abstract: The objective of link prediction for social network is to estimate the likelihood that a link exists between
two nodes x and y. There are some well-known local information-based link prediction algorithms (LILPAs)
which have been proposed to handle this essential and crucial problem in the social network analysis. However,
they can not adequately consider the so-called local information: the degrees of x and y, the number
of common neighbors of nodes x and y, and the degrees of common neighbors of x and y. In other words,
not any LILPA takes into account all the local information simultaneously. This limits the performances of
LILPAs to a certain degree and leads to the high variability of LILPAs. Thus, in order to make full use of
all the local information and obtain a LILPA with highly-predicted capability, an ordered weighted averaging
(OWA) operator based link prediction ensemble algorithm (LPEOWA) is proposed by integrating nine different
LILPAs with aggregation weights which are determined with maximum entropy method. The final experimental
results on benchmark social network datasets show that LPEOWA can obtain higher prediction accuracies
which is measured by the area under the receiver operating characteristic curve (AUC) in comparison with
nine individual LILPAs.(More)

The objective of link prediction for social network is to estimate the likelihood that a link exists between two nodes x and y. There are some well-known local information-based link prediction algorithms (LILPAs) which have been proposed to handle this essential and crucial problem in the social network analysis. However, they can not adequately consider the so-called local information: the degrees of x and y, the number of common neighbors of nodes x and y, and the degrees of common neighbors of x and y. In other words, not any LILPA takes into account all the local information simultaneously. This limits the performances of LILPAs to a certain degree and leads to the high variability of LILPAs. Thus, in order to make full use of all the local information and obtain a LILPA with highly-predicted capability, an ordered weighted averaging (OWA) operator based link prediction ensemble algorithm (LPEOWA) is proposed by integrating nine different LILPAs with aggregation weights which are determined with maximum entropy method. The final experimental results on benchmark social network datasets show that LPEOWA can obtain higher prediction accuracies which is measured by the area under the receiver operating characteristic curve (AUC) in comparison with nine individual LILPAs.

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Liu J., He Y., Hu Y., Wang X. and Shiu S. (2014). Integrating Local Information-based Link Prediction Algorithms with OWA Operator.In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 213-219. DOI: 10.5220/0004825902130219

@conference{icpram14, author={James N. K. Liu and Yu-Lin He and Yan-Xing Hu and Xi-Zhao Wang and Simon C. K. Shiu}, title={Integrating Local Information-based Link Prediction Algorithms with OWA Operator}, booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,}, year={2014}, pages={213-219}, publisher={SciTePress}, organization={INSTICC}, doi={10.5220/0004825902130219}, isbn={978-989-758-018-5}, }

TY - CONF

JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, TI - Integrating Local Information-based Link Prediction Algorithms with OWA Operator SN - 978-989-758-018-5 AU - Liu J. AU - He Y. AU - Hu Y. AU - Wang X. AU - Shiu S. PY - 2014 SP - 213 EP - 219 DO - 10.5220/0004825902130219