A sequential neural information diffusion model with structure attention

Zhitao Wang, Chengyao Chen, Wenjie Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

19 Citations (Scopus)


In this paper, we propose a novel sequential neural network with structure attention to model information diffusion. The proposed model explores both sequential nature of an information diffusion process and structural characteristics of user connection graph. The recurrent neural network framework is employed to model the sequential information. The attention mechanism is incorporated to capture the structural dependency among users, which is defined as the diffusion context of a user. A gating mechanism is further developed to effectively integrate the sequential and structural information. The proposed model is evaluated on the diffusion prediction task. The performances on both synthetic and real datasets demonstrate its superiority over popular baselines and state-of-the-art sequence-based models.

Original languageEnglish
Title of host publicationCIKM 2018 - Proceedings of the 27th ACM International Conference on Information and Knowledge Management
EditorsNorman Paton, Selcuk Candan, Haixun Wang, James Allan, Rakesh Agrawal, Alexandros Labrinidis, Alfredo Cuzzocrea, Mohammed Zaki, Divesh Srivastava, Andrei Broder, Assaf Schuster
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)9781450360142
Publication statusPublished - 17 Oct 2018
Event27th ACM International Conference on Information and Knowledge Management, CIKM 2018 - Torino, Italy
Duration: 22 Oct 201826 Oct 2018

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings


Conference27th ACM International Conference on Information and Knowledge Management, CIKM 2018


  • Information Diffusion
  • Neural Network
  • Structure Attention

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this