PyGDA is a Python library for Graph Domain Adaptation.
-
Updated
May 25, 2024 - Python
PyGDA is a Python library for Graph Domain Adaptation.
autoupdate paper list
Code for "Optimizing ZX-Diagrams with Deep Reinforcement Learning"
[KDD'2024 Survey+Tutorial] "LLM4Graph: A Survey of Large Language Models for Graphs"
Advanced Graph Clustering method documentation and implementation (From Spectral Clustering to Deep Graph Clustering)
XFlow - A Python Library for Graph Flow
Contextualizing protein representations using deep learning on protein networks and single-cell data
A python package and collection of scripts for computing protein surface meshes, chemical, electrostatic, geometric features, and building/training graph neural network models of protein-nucleic acid binding
A list of awesome GNN systems.
Paper list about hyperbolic embedding, hyperbolic models,hyperbolic applications
[IJCAI 2024] Papers about graph reduction including graph coarsening, graph condensation, graph sparsification, graph summarization, etc.
The integration of HugeGraph with artificial intelligence
Next-generation scheduling problem solver based on GNNs and Reinforcement Learning
PyHGF: A neural network library for predictive coding
🔨 🍇 💻 🚀 GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba | 一站式图计算系统
Papers about explainability of GNNs
Python package built to ease deep learning on graph, on top of existing DL frameworks.
DANCE: a deep learning library and benchmark platform for single-cell analysis
Graph Neural Network Library for PyTorch
All in One: Multi-task Prompting for Graph Neural Networks, KDD 2023.
Add a description, image, and links to the graph-neural-networks topic page so that developers can more easily learn about it.
To associate your repository with the graph-neural-networks topic, visit your repo's landing page and select "manage topics."