Projects sharing features with APPNP

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

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

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

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

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

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

This project is a collection of deep learning research papers translated into annotated code. It serves as a resource for reproducing academic research, providing implementations of transformers, diffusion models, and reinforcement learning architectures. The library distinguishes itself by using a side-by-side annotation format that combines executable Python code with descriptive markdown notes. This approach provides a structured way to explain the logic of neural network papers alongside their PyTorch-based implementations. The codebase covers several major capability areas, including ge

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

GraphSlim, a PyTorch Library for Graph Reduction.

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.

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…

A comprehensive collection of recent papers on graph deep learning

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)

This is the Pytorch implementation of NeurIPS-23 work: "Structure-free Graph Condensation (SFGC): From Large-scale Graphs to Condensed Graph-free Data".

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

Read ProteinMPNN paper.

This repository contains the Pytorch implementation code for paper:

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

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

A PyTorch and TorchDrug based deep learning library for drug pair scoring. (KDD 2022)

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

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

A PyTorch implementation of "SimGNN: A Neural Network Approach to Fast Graph Similarity Computation" (WSDM 2019).

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.

One can use the Colab to evaluate our latest models.