DSINE: Deep Structural Influence Learning via Network Embedding


  • Jianjun Wu Chinese Academy of Sciences
  • Ying Sha Chinese Academy of Sciences
  • Bo Jiang Chinese Academy of Sciences
  • Jianlong Tan Chinese Academy of Sciences




Structural representations of user social influence are critical for a variety of applications such as viral marketing and recommendation products. However, existing studies only focus on capturing and preserving the structure of relations, and ignore the diversity of influence relations patterns among users. To this end, we propose a deep structural influence learning model to learn social influence structure via mining rich features of each user, and fuse information from the aligned selfnetwork component for preserving global and local structure of the influence relations among users. Experiments on two real-world datasets demonstrate that the proposed model outperforms the state-of-the-art algorithms for learning rich representations in multi-label classification task.




How to Cite

Wu, J., Sha, Y., Jiang, B., & Tan, J. (2019). DSINE: Deep Structural Influence Learning via Network Embedding. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 10065-10066. https://doi.org/10.1609/aaai.v33i01.330110065



Student Abstract Track