User Profile Preserving Social Network Embedding

By Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang. 2017

100 citations recorded by the source corpus.

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 (UPPSNE), which incorporates user profile with network structure to jointly learn a vector representation of a social network. The theme of UPPSNE 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.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Shirui Pan
2. Guodong Long
3. Jing Jiang
4. Chengqi Zhang
5. Ruiqi Hu
6. Lina Yao
7. Philip S. Yu
8. Michael Hahn

In Information Systems

1. Jing Jiang
2. Guodong Long
3. Lina Yao
4. Shirui Pan
5. Ruiqi Hu
6. Chengqi Zhang

In Statistical and Nonlinear Physics

1. Shirui Pan
2. Chengqi Zhang
3. Guodong Long
4. Ruiqi Hu
5. Jing Jiang

Citations in the corpus

• Adversarially Regularized Graph Autoencoder (2018, methodology): “UPP-SNE employs an approximated kernel mapping scheme to exploit user pro€le features to enhance the embedding learning of users in social networks [Zhang et al., 2017b].”
• Learning Graph Embedding With Adversarial Training Methods (2019, unknown): “UPP-SNE [35] employs an approximated kernel mapping scheme to exploit user profile features to enhance the embedding learning of users in social networks.”
• Mapping Scientific Knowledge in Systematic Literature Reviews Through Embeddings and Bibliometric Techniques (2026, unknown): “Many recent studies have demonstrated that embedding models provide a robust foundation for advanced data analysis, enabling deeper insights across various thematic areas [28], [29], [30].”