Abstract
This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, user profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPP-SNE), which incorporates user profile with network structure to jointly learn a vector representation of a social network. The theme of UPP-SNE is to embed user profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.
Original language | English |
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Title of host publication | Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence |
Editors | Carles Sierra |
Place of Publication | Marina del Rey CA USA |
Publisher | Association for the Advancement of Artificial Intelligence (AAAI) |
Pages | 3378-3384 |
Number of pages | 7 |
ISBN (Electronic) | 9780999241103 |
ISBN (Print) | 9780999241110 |
DOIs | |
Publication status | Published - 2017 |
Externally published | Yes |
Event | International Joint Conference on Artificial Intelligence 2017 - Melbourne, Australia Duration: 19 Aug 2017 → 25 Aug 2017 Conference number: 26th https://ijcai-17.org/ https://www.ijcai.org/Proceedings/2017/ (Proceedings) |
Conference
Conference | International Joint Conference on Artificial Intelligence 2017 |
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Abbreviated title | IJCAI 2017 |
Country/Territory | Australia |
City | Melbourne |
Period | 19/08/17 → 25/08/17 |
Internet address |
Keywords
- Machine Learning
- Data Mining
- Feature Selection/Construction
- Unsupervised Learning