TY - GEN
T1 - Disentangled link prediction for signed social networks via disentangled representation learning
AU - Xu, Linchuan
AU - Wei, Xiaokai
AU - Cao, Jiannong
AU - Yu, Philip S.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - 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.
AB - 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.
KW - Link prediction
KW - Representation learning
KW - Signed social networks
UR - http://www.scopus.com/inward/record.url?scp=85046248544&partnerID=8YFLogxK
U2 - 10.1109/DSAA.2017.21
DO - 10.1109/DSAA.2017.21
M3 - Conference article published in proceeding or book
AN - SCOPUS:85046248544
T3 - Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
SP - 676
EP - 685
BT - Proceedings - 2017 International Conference on Data Science and Advanced Analytics, DSAA 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Data Science and Advanced Analytics, DSAA 2017
Y2 - 19 October 2017 through 21 October 2017
ER -