User Profile Preserving Social Network Embedding

Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
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
more » ... lete. 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.
doi:10.24963/ijcai.2017/472 dblp:conf/ijcai/ZhangYZZ17 fatcat:ztmxzsrpobf77ieem32hd2cmpa