Information diffusion prediction with network regularized role-based user representation learning

Zhitao Wang, Chengyao Chen, Wenjie Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

16 Citations (Scopus)


In this article, we aim at developing a user representation learning model to solve the information diffusion prediction problem in social media. The main idea is to project the diffusion users into a continuous latent space as the role-based (sender and receiver) representations, which capture unique diffusion characteristics of users. The model learns the role-based representations based on a cascade modeling objective that aims at maximizing the likelihood of observed cascades, and employs the matrix factorization objective of reconstructing structural proximities as a regularization on representations. By jointly embedding the information of cascades and network, the learned representations are robust on different diffusion data. We evaluate the proposed model on three real-world datasets. The experimental results demonstrate the better performance of the proposed model than state-of-the-art diffusion embedding and network embedding models and other popular graph-based methods.

Original languageEnglish
Article number29
JournalACM Transactions on Knowledge Discovery from Data
Issue number3
Publication statusPublished - 1 Jun 2019


  • Diffusion role
  • Information diffusion
  • Network regularization
  • Representation learning

ASJC Scopus subject areas

  • Computer Science(all)


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