User Profile Preserving Social Network Embedding

Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

81 Citations (Scopus)


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 languageEnglish
Title of host publicationProceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
EditorsCarles Sierra
Place of PublicationMarina del Rey CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Number of pages7
ISBN (Electronic)9780999241103
ISBN (Print)9780999241110
Publication statusPublished - 2017
Externally publishedYes
EventInternational Joint Conference on Artificial Intelligence 2017 - Melbourne, Australia
Duration: 19 Aug 201725 Aug 2017
Conference number: 26th (Proceedings)


ConferenceInternational Joint Conference on Artificial Intelligence 2017
Abbreviated titleIJCAI 2017
Internet address


  • Machine Learning
  • Data Mining
  • Feature Selection/Construction
  • Unsupervised Learning

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