24 Open source projects that are alternatives of or similar to gnn

PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).

[CVPR 2020 VL3] The repository for meta fine-tuning in cross-domain few-shot learning.

GemNet model in PyTorch, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)

A reading list for deep graph learning acceleration.

A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.

Pytorch implementation of CVPR2020 paper “VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation”

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

STAGIN: Spatio-Temporal Attention Graph Isomorphism Network

Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022

List of Publications in Graph Contrastive Learning

A real-time GNN-based method. Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective

An index of recommendation algorithms that are based on Graph Neural Networks.

This shows a basic implementation of the global nearest neighbour (GNN) multi target Tracker. Kalman filter is used for Tracking and Auction Algorithm for determining the assignment of measurments to filters.

A Tensorflow implementation of "Bayesian Graph Convolutional Neural Networks" (AAAI 2019).

Clean & Documented TF2 implementation of "An end-to-end deep learning architecture for graph classification" (M. Zhang et al., 2018).

A Pytorch implementation of CVPR 2020 paper: Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text

Learning cell communication from spatial graphs of cells

Causing: CAUsal INterpretation using Graphs

Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.

A Deep Graph Neural Network Architecture for Modelling Spatio-temporal Dynamics in rs-fMRI Data

Code and resources on scalable and efficient Graph Neural Networks

Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

Code for reproducing results in GraphMix paper

Must-read papers on graph neural networks (GNN)