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Title: Attributed social network embedding
Authors: Liao, Lizi
He, Xiangnan
Zhang, Hanwang
Chua, Tat-Seng
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Liao, L., He, X., Zhang, H., & Chua, T.-S. (2018). Attributed social network embedding. IEEE Transactions on Knowledge and Data Engineering, 30(12), 2257-2270. doi:10.1109/tkde.2018.2819980
Journal: IEEE Transactions on Knowledge and Data Engineering
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.
ISSN: 1041-4347
DOI: 10.1109/TKDE.2018.2819980
Rights: © 2018 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 published version is available at:
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