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
STGM: Spatio-Temporal Graph Mixformer for Traffic Forecasting
GraSeq: Graph and Sequence Fusion Learning for Molecular Property Prediction. In CIKM 2020.
Using to predict the highway traffic speed
An implementation of R-GCN model detection on IEEE-Fraud Detection Dataset,
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.
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.
The official implementation of Convergent Graph Solvers (CGS)
A project emulating a GNN model which uses EEG data to identify depression in individuals.
PyTorch implementation of GNN models
Ligand binding affinity prediction with fusion of graph neural networks and 3D structure-based complex graph
Implementation of FedGNN (Federated GNN) using pyG library
with GUG, Let's explore the Graph Neural Network!
Pytorch Geometric implementation of the "Gravity-Inspired Graph Autoencoders for Directed Link Prediction" paper.
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