Please use this identifier to cite or link to this item: https://doi.org/10.1109/TKDE.2018.2819980
Title: Attributed Social Network Embedding
Authors: Lizi Liao 
Xiangnan He 
Hanwang Zhang 
Tat-Seng Chua 
Keywords: Social Network Representation
Homophily
Deep Learning
Issue Date: 12-May-2017
Publisher: IEEE Computer Society
Citation: Lizi Liao, Xiangnan He, Hanwang Zhang, Tat-Seng Chua (2017-05-12). Attributed Social Network Embedding. IEEE Transactions on Knowledge and Data Engineering 30 (12) : 2257 - 2270. ScholarBank@NUS Repository. https://doi.org/10.1109/TKDE.2018.2819980
Abstract: Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging network structure. For social networks, besides the network structure, there also exists rich information about social actors, such as user profiles of friendship networks and textual content of citation networks. These rich attribute information of social actors reveal the homophily effect, exerting huge impacts on the formation of social networks. In this paper, we explore the rich evidence source of attributes in social networks to improve network embedding. We propose a generic Attributed Social Network Embedding framework (ASNE), which learns representations for social actors (i.e., nodes) by preserving both the structural proximity and attribute proximity. While the structural proximity captures the global network structure, the attribute proximity accounts for the homophily effect. To justify our proposal, we conduct extensive experiments on four real-world social networks. Compared to the state-of-the-art network embedding approaches, ASNE can learn more informative representations, achieving substantial gains on the tasks of link prediction and node classification. Specifically, ASNE significantly outperforms node2vec with an 8.2 percent relative improvement on the link prediction task, and a 12.7 percent gain on the node classification task. © 1989-2012 IEEE.
Source Title: IEEE Transactions on Knowledge and Data Engineering
URI: https://scholarbank.nus.edu.sg/handle/10635/168371
ISSN: 10414347
DOI: 10.1109/TKDE.2018.2819980
Appears in Collections:Staff Publications
Elements

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
Attributed Social Network Embedding.pdf2.94 MBAdobe PDF

OPEN

NoneView/Download

SCOPUSTM   
Citations

190
checked on Jun 28, 2022

Page view(s)

163
checked on Jun 23, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.