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Updated: Sep 16, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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Event prediction model combining ordinary differential equation and hypernetworks.

Ying Wang¹, Xianglin Zuo¹, Xinglin Liu²

¹College of Computer Science and Technology, Jilin University, Changchun, Jilin Province, 130012, China; Key Laboratory of Symbol Computation and Knowledge Engineering (Jilin University), Ministry of Education, Changchun, Jilin Province, 130012, China.

Neural Networks : the Official Journal of the International Neural Network Society

|July 11, 2025

Summary

This study introduces ODEH, a novel event prediction model. ODEH effectively models continuous and discrete event impacts, outperforming existing methods in network event prediction.

Keywords:

Dynamic networks Event prediction Graph neural network Ordinary differential equations Representation learning

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Area of Science:

Network Science
Machine Learning
Computational Mathematics

Background:

Modeling temporal network events is vital for user experience and commercial value.
Current methods using graph neural networks and time-series models assume events have instantaneous, unchanging impacts.
Existing approaches often overlook individual event characteristics by using a single, fixed model.

Purpose of the Study:

To propose a novel event prediction model, ODEH, that addresses limitations of existing methods.
To capture both continuous and discrete impacts of network events on nodes.
To account for the unique characteristics of individual events for personalized prediction.

Main Methods:

Constructing an ordinary differential equation (ODE) using graph neural networks to model continuous event impacts.
Employing a message passing mechanism to capture the discrete influence of events as they occur.
Utilizing a hypernetwork to fine-tune the prediction model based on event-specific information.

Main Results:

ODEH demonstrates superior performance compared to baseline methods across four datasets.
The model effectively captures the nonlinear and continuous impact of events on nodes.
Personalized event prediction is achieved by accounting for individual event characteristics.

Conclusions:

ODEH offers a significant advancement in network event prediction.
The integration of ODEs and hypernetworks provides a robust framework for modeling complex event dynamics.
The proposed method enhances the accuracy and personalization of event prediction models.