CasODE: Modeling Irregular Information Cascade via Neural Ordinary Differential Equations (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v37i13.26956Keywords:
Information Cascade, Popularity Prediction, Neural Ordinary EquationsAbstract
Predicting information cascade popularity is a fundamental problem for understanding the nature of information propagation on social media. However, existing works fail to capture an essential aspect of information propagation: the temporal irregularity of cascade event -- i.e., users' re-tweetings at random and non-periodic time instants. In this work, we present a novel framework CasODE for information cascade prediction with neural ordinary differential equations (ODEs). CasODE generalizes the discrete state transitions in RNNs to continuous-time dynamics for modeling the irregular-sampled events in information cascades. Experimental evaluations on real-world datasets demonstrate the advantages of the CasODE over baseline approaches.Downloads
Published
2023-09-06
How to Cite
Cheng, Z., Xu, X., Zhong, T., Zhou, F., & Trajcevski, G. (2023). CasODE: Modeling Irregular Information Cascade via Neural Ordinary Differential Equations (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16192-16193. https://doi.org/10.1609/aaai.v37i13.26956
Issue
Section
AAAI Student Abstract and Poster Program