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Title: Attributed Social Network Embedding
Authors: Lizi Liao 
Xiangnan He 
Hanwang Zhang 
Tat-Seng Chua 
Keywords: Social Network Representation
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.
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
ISSN: 10414347
DOI: 10.1109/TKDE.2018.2819980
Appears in Collections:Staff Publications

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