Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105662
Title: | Disentangled link prediction for signed social networks via disentangled representation learning | Authors: | Xu, L Wei, X Cao, J Yu, PS |
Issue Date: | 2017 | Source: | 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Tokyo, Japan, 19-21 October 2017, p. 676-685 | Abstract: | Link prediction is an important and interesting application for social networks because it can infer potential links among network participants. Existing approaches basically work with the homophily principle, i.e., people of similar characteristics tend to befriend each other. In this way, however, they are not suitable for inferring negative links or hostile links, which usually take place among people with different characteristics. Moreover, negative links tend to couple with positive links to form signed networks. In this paper, we thus study the problem of disentangled link prediction (DLP) for signed networks, which includes two separate tasks, i.e., inferring positive links and inferring negative links. Recently, representation learning methods have been proposed to solve the link prediction problem because the entire network structure can be encoded in representations. For the DLP problem, we thus propose to disentangle a node representation into two representations, and use one for positive link prediction and another for negative link prediction. Experiments on three real-world signed networks demonstrate the proposed disentangled representation learning (DRL) method significantly outperforms alternatives in the DLP problem. | Keywords: | Link prediction Representation learning Signed social networks |
Publisher: | Institute of Electrical and Electronics Engineers | ISBN: | 978-1-5090-5004-8 (Electronic) 978-1-5090-5005-5 (Print on Demand(PoD)) |
DOI: | 10.1109/DSAA.2017.21 | Rights: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The following publication L. Xu, X. Wei, J. Cao and P. S. Yu, "Disentangled Link Prediction for Signed Social Networks via Disentangled Representation Learning," 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), Tokyo, Japan, 2017, pp. 676-685 is available at https://doi.org/10.1109/DSAA.2017.21. |
Appears in Collections: | Conference Paper |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Xu_Disentangled_Link_Prediction.pdf | Pre-Published version | 1.11 MB | Adobe PDF | View/Open |
Page views
13
Citations as of May 19, 2024
Downloads
1
Citations as of May 19, 2024
SCOPUSTM
Citations
3
Citations as of May 17, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.