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
Using to predict the highway traffic speed
The official implementation of Convergent Graph Solvers (CGS)
GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction. In CIKM 2020.
Learning to Count Isomorphisms with Graph Neural Networks
Pytorch Geometric implementation of the "Gravity-Inspired Graph Autoencoders for Directed Link Prediction" paper.
An implementation from scratch of Graph Convolutional Networks (GCN) using Numpy
Seamless integration of sport rating systems into graph neural networks in the PyTorch environment
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.
Official code for [Neurips23] MeGraph: Capturing Long-Range Interactions by Alternating Local and Hierarchical Aggregation on Multi-Scaled Graph Hierarchy
Ligand binding affinity prediction with fusion of graph neural networks and 3D structure-based complex graph
HIV molecules classification using GNN with attention
Pytorch implementation of ProtoAU for recomandation.
Clustering Hi-C contact map using graph neural networks. Utilities and data pipelines. Created as part of Bioinformatics institute spring 2022 project
deep learning model for interacting systems
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