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Implementation and experiments of graph neural netwokrs, like gcn,graphsage,gat,etc. - GitHub - shenweichen/GraphNeuralNetwork: Implementation and ...

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the recent years. Graph neural networks, also known as deep learning on graphs, graph representation learning, or geometric deep learning have become one of the fastest-growing research topics in machine learning, especially deep learning.

Graph Neural Network Library for PyTorch. Contribute to pyg-team/pytorch_geometric development by creating an account on GitHub.

TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform. - GitHub - tensorflow/gnn: TensorFlow GNN is a library to build Graph ...

Must-read papers on graph neural networks (GNN). Contribute to thunlp/GNNPapers development by creating an account on GitHub.

Efficient Graph Neural Networks - a curated list of papers and projects - GitHub - chaitjo/awesome-efficient-gnn: Efficient Graph Neural Networks - a ...

GitHub is where people build software. More than 73 million people use GitHub to discover, fork, and contribute to over 200 million projects.

Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2. The main goal of this project is to provide a simple but flexible ...

"Relational inductive biases, deep learning, and graph networks." arXiv preprint arXiv:1806.01261 (2018). Graph Convolution Network (GCN). Defferrard, Michaël, ...

Graph Neural Networks Libraries. Deep Graph Library (DGL). A Python package that interfaces between existing tensor libraries and data being expressed as graphs ...

Platform for designing and evaluating Graph Neural Networks (GNN) - GitHub - snap-stanford/GraphGym: Platform for designing and evaluating Graph Neural ...

Graph networks are part of the broader family of "graph neural networks" (Scarselli et al., 2009). To learn more about graph networks, see our arXiv paper: ...

Class GitHub Graph Neural Networks. In the previous section, we have learned how to represent a graph using “shallow encoders”. Those techniques give us powerful expressions of a graph in a vector space, but there are limitations as well.

13.01.2022 · Issues. Pull requests. [NeurIPS 2020] "Graph Contrastive Learning with Augmentations" by Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen. self-supervised-learning pre-training graph-neural …

26 Graph Neural Networks in Anomaly Detection 561 26.2 Issues In this section, we provide a brief discussion and summary of the issues in GNN-based anomaly detection. In particular, we group them into three: (i) data-specific issues, (ii) task …

Benchmark Dataset for Graph Classification: This repository contains datasets to quickly test graph classification algorithms, such as Graph Kernels and Graph Neural Networks by Filippo Bianchi. GAM: A PyTorch implementation of “Graph Classification Using Structural Attention” (KDD 2018) by Benedek Rozemberczki.

Graph Neural Networks (GNNs) I A type of neural networks operating directly on graphs [1]. I To learn a state representation which contains information of each vertex’s neighborhood. I Notations in this tutorial Notation Description Rm m …

15.01.2021 · Graph Neural Networks(GNN) are a powerful tool for solving problems on graph-structured inputs. GNNs are a super exciting sub-area of machine learning that is getting a lot of attention and activity and some impressive results recently.Google team recently used the molecular structure of compounds along with GNNs to predict their aromaand showed that, …