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Decomposition Integral Based Generalizations of OWA Operators Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Evidential Missing Link Prediction in Uncertain Social Networks

Authors : Sabrine Mallek, Imen Boukhris, Zied Elouedi, Eric Lefevre

Published in: Information Processing and Management of Uncertainty in Knowledge-Based Systems

Publisher: Springer International Publishing

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Abstract

Link prediction is the problem of determining future or missing associations between social entities. Most of the methods have focused on social networks under a certain framework neglecting some of the inherent properties of data from real applications. These latter are usually noisy, missing or partially observed. Therefore, uncertainty is an important feature to be taken into account. In this paper, proposals for handling the problem of missing link prediction while being attentive to uncertainty are presented along with a technique for uncertain social networks generation. Uncertainty is not only handled in the graph model but also in the method itself using the assets of the belief function theory as a general framework for reasoning under uncertainty. The approach combines sampling techniques and information fusion and returns good results in real-life settings.

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Metadata
Title
Evidential Missing Link Prediction in Uncertain Social Networks
Authors
Sabrine Mallek
Imen Boukhris
Zied Elouedi
Eric Lefevre
Copyright Year
2016
Book
Information Processing and Management of Uncertainty in Knowledge-Based Systems

Print ISBN: 978-3-319-40595-7

Electronic ISBN: 978-3-319-40596-4

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-40596-4_24

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