Attributed social network embedding
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 structu...
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sg-ntu-dr.10356-1407942020-06-02T04:00:39Z Attributed social network embedding Liao, Lizi He, Xiangnan Zhang, Hanwang Chua, Tat-Seng School of Computer Science and Engineering Engineering::Computer science and engineering Social Network Representation Homophily 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. NRF (Natl Research Foundation, S’pore) 2020-06-02T04:00:39Z 2020-06-02T04:00:39Z 2018 Journal Article 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 1041-4347 https://hdl.handle.net/10356/140794 10.1109/TKDE.2018.2819980 2-s2.0-85044860469 12 30 2257 2270 en IEEE Transactions on Knowledge and Data Engineering © 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: https://doi.org/10.1109/TKDE.2018.2819980 |
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Engineering::Computer science and engineering Social Network Representation Homophily Liao, Lizi He, Xiangnan Zhang, Hanwang Chua, Tat-Seng Attributed social network embedding |
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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. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Liao, Lizi He, Xiangnan Zhang, Hanwang Chua, Tat-Seng |
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Liao, Lizi He, Xiangnan Zhang, Hanwang Chua, Tat-Seng |
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Liao, Lizi |
title |
Attributed social network embedding |
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Attributed social network embedding |
title_full |
Attributed social network embedding |
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Attributed social network embedding |
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Attributed social network embedding |
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attributed social network embedding |
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2020 |
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https://hdl.handle.net/10356/140794 |
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1681056802709438464 |