Topological recurrent neural network for diffusion prediction

Jia Wang, Vincent W. Zheng, Zemin Liu, Kevin Chen Chuan Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we study the problem of using representation learning to assist information diffusion prediction on graphs. In particular, we aim at estimating the probability of an inactive node to be activated next in a cascade. Despite the success of recent deep learning methods for diffusion, we find that they often underexplore the cascade structure. We consider a cascade as not merely a sequence of nodes ordered by their activation time stamps; instead, it has a richer structure indicating the diffusion process over the data graph. As a result, we introduce a new data model, namely diffusion topologies, to fully describe the cascade structure. We find it challenging to model diffusion topologies, which are dynamic directed acyclic graphs (DAGs), with the existing neural networks. Therefore, we propose a novel topological recurrent neural network, namely Topo-LSTM, for modeling dynamic DAGs. We customize Topo-LSTM for the diffusion prediction task, and show it improves the state-of-the-art baselines, by 20.1%-56.6% (MAP) relatively, across multiple real-world data sets.

Original languageEnglish (US)
Title of host publicationProceedings - 17th IEEE International Conference on Data Mining, ICDM 2017
EditorsGeorge Karypis, Srinivas Alu, Vijay Raghavan, Xindong Wu, Lucio Miele
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781538638347
StatePublished - Dec 15 2017
Event17th IEEE International Conference on Data Mining, ICDM 2017 - New Orleans, United States
Duration: Nov 18 2017Nov 21 2017

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other17th IEEE International Conference on Data Mining, ICDM 2017
Country/TerritoryUnited States
CityNew Orleans

ASJC Scopus subject areas

  • General Engineering


Dive into the research topics of 'Topological recurrent neural network for diffusion prediction'. Together they form a unique fingerprint.

Cite this