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AuthorWu, H.
AuthorGuan, Donghai
AuthorHan, Guangjie
AuthorYuan, Weiwei
AuthorGuizani, Mohsen
Available date2022-12-05T14:39:28Z
Publication Date2020-06-01
Publication Name2020 International Wireless Communications and Mobile Computing, IWCMC 2020
Identifierhttp://dx.doi.org/10.1109/IWCMC48107.2020.9148129
CitationWu, H., Guan, D., Han, G., Yuan, W., & Guizani, M. (2020, June). Signed Network Embedding with Dynamic Metric Learning. In 2020 International Wireless Communications and Mobile Computing (IWCMC) (pp. 533-538). IEEE.‏
ISBN9781728131290
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85089696799&origin=inward
URIhttp://hdl.handle.net/10576/36938
AbstractNetwork embedding is an important method to learn low-dimensional vector representations of nodes in networks, which has wide-ranging applications in network analysis such as link prediction. Most existing network embedding models focus on the unsigned networks with only positive links. However, networks should have both positive and negative links in practical applications such as the trust and distrust relationships in social networks. It is certain that there are different properties between positive links and negative links, which means the network embedding models designed for unsigned networks are not suitable for signed networks. In this paper, we propose SNE-DML, a signed network embedding model with dynamic metric learning. The model learns positive and negative distance metrics respectively in the training process. We conduct sign prediction experiments on three datasets and compare with seven baselines including three signed network embedding models and four state-of-the-art unsigned network embedding models. The experimental results show the effectiveness of our model.
SponsorThis research was supported by Nature Science Foundation of China (Grant No. 61672284), Nature Science Foundation of Jiangsu Province (Grant No. BK20171418), China Postdoctoral Science Foundation (Grant No. 2016M591841) and Jiangsu Planned Projects for Postdoctoral Research Funds (No. 1601225C).
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectdynamic metric learning
sign prediction
Signed network embedding
TitleSigned Network Embedding with Dynamic Metric Learning
TypeConference Paper
Pagination533-538
dc.accessType Abstract Only


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