Next Article in Journal
High-Speed Spindle Fault Diagnosis with the Empirical Mode Decomposition and Multiscale Entropy Method
Previous Article in Journal
Image Encryption Using Chebyshev Map and Rotation Equation
Article Menu

Export Article

Open AccessArticle
Entropy 2015, 17(4), 2140-2169; doi:10.3390/e17042140

Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks

1
School of Computer Science and Technology, Harbin Institute of Technology, No.92 West Da Zhi Street, Harbin, 150001, China
2
Harbin Institute of Technology Shenzhen Graduate School, HIT, HIT Campus of University Town of Shenzhen, Shenzhen, 518055, China
*
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Received: 24 January 2015 / Revised: 18 March 2015 / Accepted: 1 April 2015 / Published: 10 April 2015
View Full-Text   |   Download PDF [2535 KB, uploaded 10 April 2015]   |  

Abstract

In some online social network services (SNSs), the members are allowed to label their relationships with others, and such relationships can be represented as the links with signed values (positive or negative). The networks containing such relations are named signed social networks (SSNs), and some real-world complex systems can be also modeled with SSNs. Given the information of the observed structure of an SSN, the link prediction aims to estimate the values of the unobserved links. Noticing that most of the previous approaches for link prediction are based on the members’ similarity and the supervised learning method, however, research work on the investigation of the hidden principles that drive the behaviors of social members are rarely conducted. In this paper, the deep belief network (DBN)-based approaches for link prediction are proposed. Including an unsupervised link prediction model, a feature representation method and a DBN-based link prediction method are introduced. The experiments are done on the datasets from three SNSs (social networking services) in different domains, and the results show that our methods can predict the values of the links with high performance and have a good generalization ability across these datasets. View Full-Text
Keywords: link prediction; signed social networks; deep belief networks; unsupervised learning; feature representation link prediction; signed social networks; deep belief networks; unsupervised learning; feature representation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Liu, F.; Liu, B.; Sun, C.; Liu, M.; Wang, X. Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks. Entropy 2015, 17, 2140-2169.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top