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
STGM: Spatio-Temporal Graph Mixformer for Traffic Forecasting
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
Fiora is an in silico fragmentation algorithm for small compounds and produces simulated tandem mass spectra (MS/MS). The framework uses a graph neural network as the core module and edge prediction to identify likely bond cleavages and fragment ion intensities.
The official implementation of Convergent Graph Solvers (CGS)
with GUG, Let's explore the Graph Neural Network!
GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction. In CIKM 2020.
A project emulating a GNN model which uses EEG data to identify depression in individuals.
Learning to Count Isomorphisms with Graph Neural Networks
PyTorch implementation of GNN models
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
Pytorch implementation of ProtoAU for recomandation.
CS224W: Graph Embedding, GNNs, Recommendation Systems, and applications.
A collection of social datasets for RecBole-GNN.
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