Sign Inference for Dynamic Signed Networks via Dictionary Learning
Joint Authors
Source
Journal of Applied Mathematics
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-12-03
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Mobile online social network (mOSN) is a burgeoning research area.
However, most existing works referring to mOSNs deal with static network structures and simply encode whether relationships among entities exist or not.
In contrast, relationships in signed mOSNs can be positive or negative and may be changed with time and locations.
Applying certain global characteristics of social balance, in this paper, we aim to infer the unknown relationships in dynamic signed mOSNs and formulate this sign inference problem as a low-rank matrix estimation problem.
Specifically, motivated by the Singular Value Thresholding (SVT) algorithm, a compact dictionary is selected from the observed dataset.
Based on this compact dictionary, the relationships in the dynamic signed mOSNs are estimated via solving the formulated problem.
Furthermore, the estimation accuracy is improved by employing a dictionary self-updating mechanism.
American Psychological Association (APA)
Cen, Yi& Gu, Rentao& Ji, Yuefeng. 2013. Sign Inference for Dynamic Signed Networks via Dictionary Learning. Journal of Applied Mathematics،Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-492274
Modern Language Association (MLA)
Cen, Yi…[et al.]. Sign Inference for Dynamic Signed Networks via Dictionary Learning. Journal of Applied Mathematics No. 2013 (2013), pp.1-10.
https://search.emarefa.net/detail/BIM-492274
American Medical Association (AMA)
Cen, Yi& Gu, Rentao& Ji, Yuefeng. Sign Inference for Dynamic Signed Networks via Dictionary Learning. Journal of Applied Mathematics. 2013. Vol. 2013, no. 2013, pp.1-10.
https://search.emarefa.net/detail/BIM-492274
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-492274