CasODE: Modeling Irregular Information Cascade via Neural Ordinary Differential Equations (Student Abstract)
Zhangtao Cheng, Xovee Xu, Ting Zhong, Fan Zhou, Goce Trajcevski
Abstract
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.
BibTeX
@article{Cheng_Xu_Zhong_Zhou_Trajcevski_2024, title={CasODE: Modeling Irregular Information Cascade via Neural Ordinary Differential Equations (Student Abstract)}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26956}, DOI={10.1609/aaai.v37i13.26956}, abstractNote={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.}, number={13}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Cheng, Zhangtao and Xu, Xovee and Zhong, Ting and Zhou, Fan and Trajcevski, Goce}, year={2024}, month={Jul.}, pages={16192-16193} }