To address these challenges, we propose an innovative model called spatial-temporal graph neural ordinary differential equations (STG-NODE). First, in the data preprocessing stage, the dynamic time warping (DTW)
We combine it with a graph neural ordinary differential equations (ODEs) formalism to optimize the system dynamics in embedding space to solve a downstream prediction task. Once the dynamics is learned, embedding generation for novel datasets is done by solving the ODEs in time using a numerical ...
Neural GDEs rely ondglandtorchdiffeq. NOTE: Neural GDE model zoo and additional tutorials are included in thetorchdynlibrary:link If you find our work useful, consider citing us: @article{poli2019graph, title={Graph Neural Ordinary Differential Equations}, author={Poli, Michael and Massaroli, ...
To evaluate our proposed method, we compared it with several state-of-the-art approaches and performed ablation studies to show the impact of GSP. These baseline models used for comparison were Hybrid Convolutional Recurrent Neural Networks (CNN, RNN, 2D CNN-RNN, and 3D-CNN-RNN)26, EEGNet34,...
This study introduces a novel fault detection framework using graph neural ordinary differential equations. Show abstract Distributed incipient fault detection with causality-based multi-perspective subblock partitioning for large-scale nonlinear processes 2024, Process Safety and Environmental Protection Citation...
Note this architecture can generalize most GNN models while there are also exceptions, for example, NDCN (Zang and Wang, 2020) combines ordinary differential equation systems (ODEs) and GNNs. It can be regarded as a continuous-time GNN model which integrates GNN layers over continuous time ...
Graph Neural Ordinary Differential Equations, Michael Poli, Stefano Massaroli, Junyoung Park, Atsushi Yamashita, Hajime Asama, Jinkyoo Park FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks, Md. Khaledur Rahman, Majedul Haque Sujon, , Ariful Azad An Efficient ...
Graph Neural Ordinary Differential Equations, Michael Poli, Stefano Massaroli, Junyoung Park, Atsushi Yamashita, Hajime Asama, Jinkyoo Park FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks, Md. Khaledur Rahman, Majedul Haque Sujon, , Ariful Azad An Efficient ...
Knowledge Graph Representation Learning using Ordinary Differential EquationsMojtaba Nayyeri, Chengjin Xu, Franca Hoffmann, Mirza Mohtashim Alam, Jens Lehmann and sahar vahdati Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERTZaiqiao Meng, Fangyu Liu, Thomas Clark, Ehsan Shareghi...
Temporal knowledge graph fusion with neural ordinary differential equations for the predictive maintenance of electromechanical equipment Knowledge-Based Systems Volume 317, 23 May 2025, Page 113450 Purchase options CorporateFor R&D professionals working in corporate organizations. Academic and personalFor academ...