Open-source alternatives to Tkipf Gae

A PyTorch Implementation of "Watch Your Step: Learning Node Embeddings via Graph Attention" (NeurIPS 2018).

A PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019).

A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).

A PyTorch implementation of "Graph Classification Using Structural Attention" (KDD 2018).

A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)

An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).

The official PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf '21)

A PyTorch implementation of "Semi-Supervised Graph Classification: A Hierarchical Graph Perspective" (WWW 2019)

A PyTorch implementation of "Signed Graph Convolutional Network" (ICDM 2018).

A PyTorch Implementation of "SINE: Scalable Incomplete Network Embedding" (ICDM 2018).

KDD 2022 The implementation for "Condensing Graphs via One-Step Gradient Matching" on graph classification is shown below. For node classification, please refer to link.

A graph attention network as an energy-based model for protein conformation.

ICLR 2022 The PyTorch implementation for "Graph Condensation for Graph Neural Networks" is provided under the main directory.

This repository contains the Pytorch implementation code for paper:

Read ProteinMPNN paper.

Pytorch implementation of the Attention-based Graph Neural Network(AGNN)

A comprehensive collection of recent papers on graph deep learning

Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)

pypi-image:https://badge.fury.io/py/dive-into-graphs.svg pypi-url:https://pypi.org/project/dive-into-graphs/ docs-image: https://readthedocs.org/projects/diveintographs/badge/?version=latest docs-url: https://diveintographs.readthedocs.io/en/latest/?badge=latest…

DGL is a Python library for building and training graph neural networks. It functions as a graph message passing framework and a geometric deep learning tool, enabling the development of models that analyze graph-structured data. The library is designed for large-scale graph processing, utilizing distributed training and neighbor sampling to handle datasets with billions of edges. It provides specialized support for heterogeneous graph modeling, allowing for the representation of complex real-world entities with multiple node and edge types. Its capabilities cover a wide range of graph tasks

Implementation of equivariant GVP-GNNs as described in Learning from Protein Structure with Geometric Vector Perceptrons by B Jing, S Eismann, P Suriana, RJL Townshend, and RO Dror.

GraphSlim, a PyTorch Library for Graph Reduction.

SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network

1. Running the code 2. Dependencies

One can use the Colab to evaluate our latest models.