Link Prediction in Complex Networks: A Mutual Information Perspective

Fei Tan, Yongxiang Xia and Boyao Zhu

PLOS ONE, 2014, vol. 9, issue 9, 1-8

Abstract: Topological properties of networks are widely applied to study the link-prediction problem recently. Common Neighbors, for example, is a natural yet efficient framework. Many variants of Common Neighbors have been thus proposed to further boost the discriminative resolution of candidate links. In this paper, we reexamine the role of network topology in predicting missing links from the perspective of information theory, and present a practical approach based on the mutual information of network structures. It not only can improve the prediction accuracy substantially, but also experiences reasonable computing complexity.

Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107056 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 07056&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0107056

DOI: 10.1371/journal.pone.0107056

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().