274 Open source projects that are alternatives of or similar to Pro-GNN

Python toolbox to evaluate graph vulnerability and robustness (CIKM 2021)

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

A lightweight implementation of Walklets from "Don't Walk Skip! Online Learning of Multi-scale Network Embeddings" (ASONAM 2017).

Code for the AAAI 2022 publication "Well-classified Examples are Underestimated in Classification with Deep Neural Networks"

Papers about explainability of GNNs

Graph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for evaluating the adversarial robustness of Graph Machine Learning.

Implementation of paper "Self-supervised Learning on Graphs:Deep Insights and New Directions"

Implement of DiGCN, NeurIPS-2020

[CVPR 2020] Semi-Supervised Semantic Segmentation with Cross-Consistency Training.

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

Weakly- and Semi-Supervised Panoptic Segmentation (ECCV18)

A curated collection of adversarial attack and defense on graph data.

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

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.

Python codes for weakly-supervised learning

Reimplementation of "Realistic Evaluation of Deep Semi-Supervised Learning Algorithms"

Unsupervised Data Augmentation (UDA)

The standard package for machine learning with noisy labels, finding mislabeled data, and uncertainty quantification. Works with most datasets and models.

Code for Adversarial Training Methods for Semi-Supervised Text Classification

No description or website provided.

Code for reproducing ICT ( published in IJCAI 2019)

Active learning for deep object detection using YOLO

speech to text with self-supervised learning based on wav2vec 2.0 framework

Pytorch implementation of Virtual Adversarial Training

[CVPR 2021] DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

NeurIPS 2019: HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs

Autoencoders for Link Prediction and Semi-Supervised Node Classification (DSAA 2018)

[CVPR 2022] ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation

VOSK Speech Recognition Toolkit

Group satellites into constellations such that their average observation coverage is maximized

[ICCV 2019]Aggregation via Separation: Boosting Facial Landmark Detector with Semi-Supervised Style Transition

Disentagnled Graph Collaborative Filtering, SIGIR2020

A PyTorch implementation of Deep SAD, a deep Semi-supervised Anomaly Detection method.

GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification

Pytorch Implementation of "Adversarial Learning For Semi-Supervised Semantic Segmentation" for ICLR 2018 Reproducibility Challenge

odr.chalmers.se/handle/20.500.12380/256629?locale=en

Implementation with some extensions of the paper "Snowball: Extracting Relations from Large Plain-Text Collections" (Agichtein and Gravano, 2000)

Visualization tool for Graph Neural Networks

Pytorch Implementation of the paper MixMatch: A Holistic Approach to Semi-Supervised Learning (https://arxiv.org/pdf/1905.02249.pdf)

All Day DevOps - Automated Infrastructure Security Monitoring and Defence (ELK + AWS Lambda)

Code and resources on scalable and efficient Graph Neural Networks

Source code and dataset of the NeurIPS 2020 paper "Graph Random Neural Network for Semi-Supervised Learning on Graphs"

Official PyTorch implementation of "Towards Deeper Graph Neural Networks" [KDD2020]

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach, AAAI2020

Pytorch implementation of "Graph Convolutional Networks for Text Classification"

Demo of RouteNet in ACM SIGCOMM'19

Semi-supervised Medical Image Segmentation through Dual-task Consistency

The official code and datasets for "Zero-Shot Multi-View Indoor Localization via Graph Location Networks" (ACMMM 2020)

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).

Protein-compound affinity prediction through unified RNN-CNN

An application to catch, search and analyze HTTP secure headers.

I try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers

Code for paper "Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation"

A state-of-the-art semi-supervised method for image recognition

PseudoLabel 2013, VAT, PI model, Tempens, MeanTeacher, ICT, MixMatch, FixMatch

PyTorch implementation of Adversarially Learned Inference (BiGAN).

Pytorch implementation of Conditional Image Synthesis with Auxiliary Classifier GANs

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

Semi-supervised GAN in "Improved Techniques for Training GANs"

Code for Universal Semi-Supervised Semantic Segmentation models paper accepted in ICCV 2019