CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)
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Updated
Feb 1, 2024 - Python
CogDL: A Comprehensive Library for Graph Deep Learning (WWW 2023)
The PyTorch 1.6 and Python 3.7 implementation for the paper Graph Convolutional Networks for Text Classification
Source Code of NeurIPS21 and T-PAMI24 paper: Recognizing Vector Graphics without Rasterization
Reconstruct billions of particle trajectories with graph neural networks
GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction. In CIKM 2020.
GNN training in kubeflow.
Using to predict the highway traffic speed
An implementation from scratch of Graph Convolutional Networks (GCN) using Numpy
The official implementation of Convergent Graph Solvers (CGS)
This paper explores the idea of using heterogeneous graph neural networks (Het-GNN) to partition old legacy monoliths into candidate microservices. We additionally take membership constraints that come from a subject matter expert who has deep domain knowledge of the application.
Seamless integration of sport rating systems into graph neural networks in the PyTorch environment
Ligand binding affinity prediction with fusion of graph neural networks and 3D structure-based complex graph
Pytorch implementation of ProtoAU for recomandation.
Pytorch Geometric implementation of the "Gravity-Inspired Graph Autoencoders for Directed Link Prediction" paper.
Official code for [Neurips23] MeGraph: Capturing Long-Range Interactions by Alternating Local and Hierarchical Aggregation on Multi-Scaled Graph Hierarchy
Inversion Symmetry-aware Directional PaiNN
deep learning model for interacting systems
Machine Learning in predictioning the atomization energies.
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