Enhanced Microblog Network Representation with User-Generated Content

Abstract

The real-world networks are so sparse that only utilizing the limited edges cannot capture valuable user feature fully. However, the existing network embedding methods usually took the network structure into consideration merely. Therefore, inspired by the assumption that there exist latent relationships between those who generate similar text information, we proposed to enhance the original network structures by incorporating the latent relationships extracted from user-generated content into the raw network topology and furthermore to learn the representation for the revised network. Eventually, we evaluate our proposed method in two inference tasks and the experiment results demonstrate that the network representation generated by our enhanced network embedding method has a better performance than the baseline method on dataset provided by Sina Microblog.