Joint Learning of User Representation with Diffusion Sequence and Network Structure

Zhitao Wang, Chengyao Chen, Wenjie Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Information sharing behavior and social link building behavior have shown strong correlation on social media. The aim of this article is to explore this correlation for simultaneously modeling and predicting sharing behavior in information diffusion sequences and linking behavior in social network, which correspond to information diffusion prediction and social link prediction problems. To achieve this goal, we propose a joint user representation learning model to characterize the two correlated behaviors in a shared latent space. The proposed model learns user representations via two maximum likelihood estimation objectives defined on observed information diffusion sequences and social network structure respectively and incorporates them in a unified framework. A multi-task learning algorithm is designed for efficient model optimization. Based on the learned representations, the model can be directly applied to predicting diffusion processes and inferring unobserved social links at the same time. We evaluate the proposed model on two real social media datasets with extensive experiments. The model consistently achieves significant improvements over the state-of-the-art approaches on diffusion prediction and link prediction tasks. The better robustness of our model in further ablation studies demonstrates that capturing the behavior correlation in the shared representation space is beneficial.

Original languageEnglish
Pages (from-to)1275-1287
Number of pages13
JournalIEEE Transactions on Knowledge and Data Engineering
Volume34
Issue number3
DOIs
Publication statusPublished - 1 Mar 2022

Keywords

  • Information diffusion prediction
  • Link prediction
  • Representation learning

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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