Geometric Deep Learning Extension Library for PyTorch
-
Updated
Sep 27, 2020 - Python
#
graph-neural-networks
Here are 292 public repositories matching this topic...
Python package built to ease deep learning on graph, on top of existing DL frameworks.
-
Updated
Sep 28, 2020 - Python
links to conference publications in graph-based deep learning
deep-learning
graph
neural-networks
graph-convolutional-networks
graph-neural-networks
graph-representation-learning
conference-publications
-
Updated
Sep 27, 2020 - Jupyter Notebook
A distributed graph deep learning framework.
deep-learning
graph
network-embedding
random-walk
graph-convolutional-networks
gcn
node2vec
graph-embedding
graph-learning
graphsage
graph-neural-networks
ggnn
-
Updated
Jul 29, 2020 - C++
Graph Neural Networks with Keras and Tensorflow 2.
-
Updated
Aug 25, 2020 - Python
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
-
Updated
Jun 13, 2020 - Jupyter Notebook
Repository for benchmarking graph neural networks
deep-learning
pytorch
benchmark-framework
graph-neural-networks
graph-representation-learning
dgl
graph-deep-learning
-
Updated
Aug 29, 2020 - Jupyter Notebook
SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
-
Updated
Aug 26, 2020 - Python
How Powerful are Graph Neural Networks?
-
Updated
Aug 6, 2020 - Python
ktrain is a Python library that makes deep learning and AI more accessible and easier to apply
python
nlp
machine-learning
computer-vision
deep-learning
tensorflow
keras
tabular-data
graph-neural-networks
-
Updated
Sep 24, 2020 - Jupyter Notebook
Pytorch Repo for "DeepGCNs: Can GCNs Go as Deep as CNNs?" ICCV2019 Oral https://www.deepgcns.org
-
Updated
Sep 28, 2020 - Python
Tensorflow Repo for "DeepGCNs: Can GCNs Go as Deep as CNNs?" ICCV2019 Oral https://www.deepgcns.org
-
Updated
Jan 23, 2020 - Python
Benchmark datasets, data loaders, and evaluators for graph machine learning
-
Updated
Sep 12, 2020 - Python
KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019
knowledge-graph
recommender-system
graph-attention-networks
graph-neural-networks
kdd2019
high-order-connectivity
knowledge-based-recommendation
knowledge-aware-recommendation
explainable-recommendation
embedding-propagation
knowledge-graph-dataset
knowledge-graph-for-recommendation
-
Updated
Aug 5, 2020 - Python
resources for graph convolutional networks （图卷积神经网络相关资源）
-
Updated
Jul 12, 2019
A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).
deep-learning
neural-network
clustering
community-detection
pytorch
deepwalk
louvain
metis
graph-convolutional-networks
gcn
graph-clustering
node2vec
node-classification
graphsage
graph-neural-networks
graph2vec
diff2vec
gemsec
musae
graph-convolution
-
Updated
Sep 24, 2020 - Python
[AAAI 2019] Source code and datasets for "Session-based Recommendation with Graph Neural Networks"
-
Updated
Jul 24, 2019 - Python
CogDL: An Extensive Research Toolkit for Graphs
leaderboard
pytorch
link-prediction
graph-embedding
graph-classification
node-classification
graph-neural-networks
pytorch-geometric
-
Updated
Sep 28, 2020 - Python
Source code and dataset for ACL 2019 paper "Cognitive Graph for Multi-Hop Reading Comprehension at Scale"
-
Updated
Mar 22, 2020 - Python
A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)
machine-learning
research
deep-learning
tensorflow
sklearn
pytorch
deepwalk
convolutional-neural-networks
representation-learning
gcn
iclr
graph-representation
node2vec
graph-convolutional-network
graphsage
graph-neural-networks
graph-wavelet
graph-convolution
gwnn
pygsp
-
Updated
Sep 24, 2020 - Python
machine-learning
natural-language-processing
computer-vision
deep-learning
generative-model
semi-supervised-learning
graph-neural-networks
-
Updated
Aug 24, 2020
PyTorch Extension Library of Optimized Graph Cluster Algorithms
-
Updated
Sep 25, 2020 - C++
Lanczos Network, Graph Neural Networks, Deep Graph Convolutional Networks, Deep Learning on Graph Structured Data, QM8 Quantum Chemistry Benchmark, ICLR 2019
-
Updated
Oct 18, 2019 - Python
Efficient Graph Generation with Graph Recurrent Attention Networks, Deep Generative Model of Graphs, Graph Neural Networks, NeurIPS 2019
-
Updated
Jul 17, 2020 - C++
Attention Guided Graph Convolutional Networks for Relation Extraction (authors' PyTorch implementation for the ACL19 paper)
nlp
deep-learning
information-extraction
graph-convolutional-networks
relation-extraction
graph-neural-networks
-
Updated
Aug 25, 2020 - Python
A pytorch adversarial library for attack and defense methods on images and graphs
machine-learning
deep-neural-networks
deep-learning
defense
graph-mining
graph-convolutional-networks
adversarial-examples
adversarial-attacks
graph-neural-networks
-
Updated
Sep 20, 2020 - Python
A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).
machine-learning
deep-learning
tensorflow
word2vec
sklearn
torch
pytorch
deepwalk
matrix-factorization
attention
nips
node2vec
graph-neural-networks
graph-representation-learning
structural-attention
implicit-factorization
walklet
graph-attention
neurips
neurips-2018
-
Updated
Sep 24, 2020 - Python
Strategies for Pre-training Graph Neural Networks
-
Updated
May 31, 2020 - Python
Graph convolutional neural network for multirelational link prediction
deep-learning
pharmacology
embeddings
representation-learning
graph-convolutional-networks
graph-neural-networks
-
Updated
Sep 25, 2020 - Jupyter Notebook
Improve this page
Add a description, image, and links to the graph-neural-networks topic page so that developers can more easily learn about it.
Add this topic to your repo
To associate your repository with the graph-neural-networks topic, visit your repo's landing page and select "manage topics."
Description
Currently our unit tests are disorganized and each test creates example StellarGraph graphs in different or similar ways with no sharing of this code.
This issue is to improve the unit tests by making functions to create example graphs available to all unit tests by, for example, making them pytest fixtures at the top level of the tests (see https://docs.pytest.org/en/latest/