211 Open source projects that are alternatives of or similar to awesome-efficient-gnn

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

A reading list for deep graph learning acceleration.

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

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

links to conference publications in graph-based deep learning

Subgraph Neural Networks (NeurIPS 2020)

Codes for "Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks" paper

A curated list of awesome graph & self-supervised-learning-based recommendation.

[GRL+ @ ICML 2020] PyTorch implementation for "Deep Graph Contrastive Representation Learning" (https://arxiv.org/abs/2006.04131v2)

A distributed graph deep learning framework.

List of Publications in Graph Contrastive Learning

Visualization tool for Graph Neural Networks

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

[ECCV 2020] Scale Adaptive Network: Learning to Learn Parameterized Classification Networks for Scalable Input Images

Implementation of the WSDM 2021 paper "Node Similarity Preserving Graph Convolutional Networks"

Tutorials for Machine Learning on Graphs

Inductive relation prediction by subgraph reasoning, ICML'20

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).

Graph Neural Network Library for PyTorch

The PyTorch implementation of Directed Graph Contrastive Learning (DiGCL), NeurIPS-2021

Quaternion Graph Neural Networks (ACML 2021) (Pytorch and Tensorflow)

Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning (authors' MXNet implementation for the TACL19 paper)

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

ProteinGCN: Protein model quality assessment using Graph Convolutional Networks

StellarGraph - Machine Learning on Graphs

A collection of important graph embedding, classification and representation learning papers with implementations.

[WWW 2021] Source code for "Graph Contrastive Learning with Adaptive Augmentation"

Representation Learning on Graphs with Jumping Knowledge Networks

Summary of open source code for deep learning models in the field of traffic prediction

A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which appeared in The International Workshop on Self-Supervised Learning for the Web (SSL'21) @ the Web Conference 2021 (WWW'21).

Knowledge Distillation using Tensorflow

Official PyTorch implementation of "Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets" (ICLR 2021)

Awesome Knowledge Distillation

Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.

An Object Detection Knowledge Distillation framework powered by pytorch, now having SSD and yolov5.

Implementation for "3D human pose, shape and texture from low-resolution images and videos", TPAMI 2021

The codes for recent knowledge distillation algorithms and benchmark results via TF2.0 low-level API

Model optimizer used in Adlik.

Code for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022)

BigGAN; Knowledge Distillation; Black-Box; Fast Training; 16x compression

Focal and Global Knowledge Distillation for Detectors (CVPR 2022)

Material for the EPFL master course "A Network Tour of Data Science", edition 2019.

[CVPR 2020] L2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks

Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks

Efficient-Computing

noisy labels; missing labels; semi-supervised learning; entropy; uncertainty; robustness and generalisation.

Implements the ideas presented in https://arxiv.org/pdf/2004.11362v1.pdf by Khosla et al.

This is the official implementation for the AAAI-2021 paper (Cross-Layer Distillation with Semantic Calibration).

Reimplementing cool papers in PyTorch...

Authors official PyTorch implementation of the "DnS: Distill-and-Select for Efficient and Accurate Video Indexing and Retrieval" [IJCV 2022]

[IEEE Access] "Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-constrained Edge Computing Systems" and [ACM MobiCom HotEdgeVideo 2019] "Distilled Split Deep Neural Networks for Edge-assisted Real-time Systems"

A Fast Knowledge Distillation Framework for Visual Recognition

Adversarial Adaptation with Distillation for BERT Unsupervised Domain Adaptation

OpenMMLab Model Compression Toolbox and Benchmark.

GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification

Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]

ZSKD with PyTorch

Disentagnled Graph Collaborative Filtering, SIGIR2020

Mobile(iOS) Text-to-Image search powered by multimodal semantic representation models(e.g., OpenAI's CLIP). Accepted at NAACL 2022.

Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons (AAAI 2019)