Link prediction in signed networks based on connection degree View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-05

AUTHORS

Xiao Chen, Jing-Feng Guo, Xiao Pan, Chunying Zhang

ABSTRACT

Link prediction has recently received considerable attention in signed networks. Most of the existing methods assume that the signed network topology is certain, such as network structure, entities relations and entities attributes. However, the assumption is not applicable, since the signed network is uncertain. As a result, the prediction accuracy cannot be ensured if the uncertainty of the signed networks is ignored. In this paper, we regard the signed network as an identical-discrepancy-contrary system employing the set pair theory, and propose a new link prediction measure SNCD which integrates both the certain and uncertain relations, local and global information at the same time. After a series of experiment, the experimental results show that our proposed method provides better prediction accuracy and correctness. More... »

PAGES

1747-1757

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12652-017-0613-2

DOI

http://dx.doi.org/10.1007/s12652-017-0613-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092689675


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