Python Graph-algorithms Resources

中文开放信息抽取系统, open-information-extraction-system, build open-knowledge-graph(SPO, subject-predicate-object) by pyltp(version==3.4.0)

A Python package implementing various colour checker detection algorithms and related utilities.

PyGCL: Graph Contrastive Learning for PyTorch PyGCL is an open-source library for graph contrastive learning (GCL), which features modularized GCL com

HKD Code for ICCV 2021 paper "Distilling Holistic Knowledge with Graph Neural Networks" cifia-100 result The implementation of compared methods are ba

40 Algorithms Every Programmer Should Know, published by Packt

Off-Policy Multi-Agent Reinforcement Learning (MARL) Algorithms This repository contains implementations of various off-policy multi-agent reinforceme

Optimization as a Model for Few-Shot Learning This repo provides a Pytorch implementation for the Optimization as a Model for Few-Shot Learning paper.

A PyTorch Implementation of GGNN This is a PyTorch implementation of the Gated Graph Sequence Neural Networks (GGNN) as described in the paper Gated G

ConvE Convolutional 2D Knowledge Graph Embeddings resources. Paper: Convolutional 2D Knowledge Graph Embeddings Used in the paper, but do not use thes

GNN4Traffic - This is the repository for the collection of Graph Neural Network for Traffic Forecasting

A Python-based application demonstrating various search algorithms, namely Depth-First Search (DFS), Breadth-First Search (BFS), and the A* Search (using the Manhattan Distance Heuristic)

Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks (SDPoint) This repository contains the cod

Table of Contents Table of Contents Local Planner Visualization Project (LPVP) Features Installation/Usage Local Planners Probabilistic Roadmap (PRM)

Real-ESRGAN Colab Demo for Real-ESRGAN . Portable Windows executable file. You can find more information here. Real-ESRGAN aims at developing Practica

TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network (SIGGRAPH 2020) About The goal of our research problem is illustrated below: give

Open-source tool for exploring, labeling, and monitoring data for AI projects

pytorch-flows A PyTorch implementations of Masked Autoregressive Flow and some other invertible transformations from Glow: Generative Flow with Invert

Deep Reinforcement Learning Algorithms with PyTorch This repository contains PyTorch implementations of deep reinforcement learning algorithms and env

Graph Wavelet Neural Network ⠀⠀ A PyTorch implementation of Graph Wavelet Neural Network (ICLR 2019). Abstract We present graph wavelet neural network

Attention Walk ⠀⠀ A PyTorch Implementation of Watch Your Step: Learning Node Embeddings via Graph Attention (NIPS 2018). Abstract Graph embedding meth

SGCN ⠀ A PyTorch implementation of Signed Graph Convolutional Network (ICDM 2018). Abstract Due to the fact much of today's data can be represented as

Scalable Incomplete Network Embedding ⠀⠀ A PyTorch implementation of Scalable Incomplete Network Embedding (ICDM 2018). Abstract Attributed network em

GAM ⠀⠀ A PyTorch implementation of Graph Classification Using Structural Attention (KDD 2018). Abstract Graph classification is a problem with practic

TuckER: Tensor Factorization for Knowledge Graph Completion This codebase contains PyTorch implementation of the paper: TuckER: Tensor Factorization f

SimGNN ⠀⠀⠀ A PyTorch implementation of SimGNN: A Neural Network Approach to Fast Graph Similarity Computation (WSDM 2019). Abstract Graph similarity s

APPNP ⠀ A PyTorch implementation of Predict then Propagate: Graph Neural Networks meet Personalized PageRank (ICLR 2019). Abstract Neural message pass

MixHop and N-GCN ⠀ A PyTorch implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019)

Splitter ⠀⠀ A PyTorch implementation of Splitter: Learning Node Representations that Capture Multiple Social Contexts (WWW 2019). Abstract Recent inte

CapsGNN ⠀⠀ A PyTorch implementation of Capsule Graph Neural Network (ICLR 2019). Abstract The high-quality node embeddings learned from the Graph Neur

SEAL ⠀⠀⠀ A PyTorch implementation of Semi-Supervised Graph Classification: A Hierarchical Graph Perspective (WWW 2019) Abstract Node classification an

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

On the Bottleneck of Graph Neural Networks and its Practical Implications This is the official implementation of the paper: On the Bottleneck of Graph

Library | Paper | Slack We released two versions of OAG-BERT in CogDL package. OAG-BERT is a heterogeneous entity-augmented academic language model wh

ElasticGNN This repository includes the official implementation of ElasticGNN in the paper "Elastic Graph Neural Networks" [ICML 2021]. Xiaorui Liu, W

PGExplainer This is a Tensorflow implementation of the paper: Parameterized Explainer for Graph Neural Network https://arxiv.org/abs/2011.04573 NeurIP

Решения и подсказки к тренировке по алгоритмам от Яндекса Что есть внутри Решения с подсказками и комментариями; рекомендую сначала смотреть md файл п

A collection of research papers and software related to explainability in graph machine learning.

PyKEEN PyKEEN (Python KnowlEdge EmbeddiNgs) is a Python package designed to train and evaluate knowledge graph embedding models (incorporating multi-m

Binance Report Bot The intent of this bot is to take a snapshot of your binance wallet, e.g. the current balances and store it for further plotting. I

CLIPPER: A Graph-Theoretic Framework for Robust Data Association Data association is a fundamental problem in robotics and autonomy. CLIPPER provides

Self-supervised Graph-level Representation Learning with Local and Global Structure Introduction This project is an implementation of ``Self-supervise

Heterogeneous INteract and aggreGatE (GraphHINGE) This is a pytorch implementation of GraphHINGE model. This is the experiment code in the following w

DI-HPC: Decision Intelligence - High Performance Computation DI-HPC is an acceleration operator component for general algorithm modules in reinforceme

Box is a text-based visual programming language inspired by Unreal Engine blueprint function graphs. $ cat factorial.box ┌─ƒ(Factorial)───┐

MooGBT is a library for Multi-objective optimization in Gradient Boosted Trees. MooGBT optimizes for multiple objectives by defining constraints on sub-objective(s) along with a primary objective. The constraints are defined as upper bounds on sub-objective loss function. MooGBT uses a Augmented Lagrangian(AL) based constrained optimization framework with Gradient Boosted Trees, to optimize for multiple objectives.

GCResNet PyTorch implementation of Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution, IJCNN 2021. The code will

This is my reading list for my PhD in AI, NLP, Deep Learning and more.

GPRGNN This is the source code for our ICLR2021 paper: Adaptive Universal Generalized PageRank Graph Neural Network. Hidden state feature extraction i

Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics This repository is the official PyTorch implementation of "Physics-aware Differ

This repository contains FEDOT - an open-source framework for automated modeling and machine learning (AutoML). It can build custom modeling pipelines for different real-world processes in an automated way using an evolutionary approach. FEDOT supports classification (binary and multiclass), regression, clustering, and time series prediction tasks.

Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about Machine Learning

Learning Canonical Representations for Scene Graph to Image Generation (ECCV 2020) Roei Herzig*, Amir Bar*, Huijuan Xu, Gal Chechik, Trevor Darrell, A

Graph-to-Graph Transformers Self-attention models, such as Transformer, have been hugely successful in a wide range of natural language processing (NL

Graph4NLP Graph4NLP is an easy-to-use library for R&D at the intersection of Deep Learning on Graphs and Natural Language Processing (i.e., DLG4NLP).

Graph Transformer - Pytorch Implementation of Graph Transformer in Pytorch, for potential use in replicating Alphafold2. This was recently used by bot

A ready-to-use framework of latest models for structured (tabular) data learning with PyTorch. Applications include recommendation, CRT prediction, healthcare analytics, and etc.

ToR[e]cSys is a PyTorch Framework to implement recommendation system algorithms, including but not limited to click-through-rate (CTR) prediction, learning-to-ranking (LTR), and Matrix/Tensor Embedding. The project objective is to develop a ecosystem to experiment, share, reproduce, and deploy in real world in a smooth and easy way (Hope it can be done).

DeepCDR Cancer Drug Response Prediction via a Hybrid Graph Convolutional Network This work has been accepted to ECCB2020 and was also published in the

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

FinGAT: A Financial Graph Attention Networkto Recommend Top-K Profitable Stocks This is our implementation for the paper: FinGAT: A Financial Graph At

Random Walk Graph Neural Networks This repository is the official implementation of Random Walk Graph Neural Networks. Requirements Code is written in

JittorVis - Visual understanding of deep learning model.

AWS_ORG_MAPPER This tool uses sso-oidc to authenticate to the AWS organization. Once authenticated the tool will attempt to enumerate all users and ro

We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an underlying PDE.

The final code for OGB Installation Requirements: ogb=1.3.1 torch=1.7.0 torch-geometric=1.7.0 torch-scatter=2.0.6 torch-sparse=0.6.9 Baseline models T

LMs for biomedical KG completion This repository contains code to run the experiments described in: Scientific Language Models for Biomedical Knowledg

Graph Contrastive Learning Automated PyTorch implementation for Graph Contrastive Learning Automated [talk] [poster] [appendix] Yuning You, Tianlong C

Toy implementations of some popular ML optimizers using Python/JAX

This is the repo for the paper: Leveraging Two Types of Global Graph for Sequential Fashion Recommendation Requirements OS: Ubuntu 16.04 or higher ver

Graphormer By Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng*, Guolin Ke, Di He*, Yanming Shen and Tie-Yan Liu. This repo is the official impl

PyGAS: Auto-Scaling GNNs in PyG PyGAS is the practical realization of our G NN A uto S cale (GAS) framework, which scales arbitrary message-passing GN

Graph-to-Tree Learning for Solving Math Word Problems PyTorch implementation of Graph based Math Word Problem solver described in our ACL 2020 paper G

MuarAugment Description MuarAugment is a package providing the easiest way to a state-of-the-art data augmentation pipeline. How to use You can instal

A Curated Collection of Awesome Python Scripts that will make you go wow. This repository will help you in getting those green squares. Hop in and enjoy the journey of open source. 🚀

MATP-solver Simulator collision check path step random initial states or given states Traditional method Seperate A* algorithem Confict-based Search S

[TensorFlow] Protein Interface Prediction using Graph Convolutional Networks Unofficial TensorFlow implementation of Protein Interface Prediction usin

GSDN-F and GSDN-EF This repository provides a reference implementation of GSDN-F and GSDN-EF as described in the paper "Understanding Graph Neural Net

Degree-Quant This repo provides a clean re-implementation of the code associated with the paper Degree-Quant: Quantization-Aware Training for Graph Ne

ProcrustEs-KGE Paddle implementation for Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis 🙈 A more detailed re

How Attentive are Graph Attention Networks? This repository is the official implementation of How Attentive are Graph Attention Networks?. The PyTorch

SparseML is a toolkit that includes APIs, CLIs, scripts and libraries that apply state-of-the-art sparsification algorithms such as pruning and quantization to any neural network. General, recipe-driven approaches built around these algorithms enable the simplification of creating faster and smaller models for the ML performance community at large.

SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization This is the code for our paper ``SumGNN: Multi-typed Drug

GCN-LPA This repository is the implementation of GCN-LPA (arXiv): Unifying Graph Convolutional Neural Networks and Label Propagation Hongwei Wang, Jur

Geometry-Aware Gradient Algorithms for Neural Architecture Search This repository contains the code required to run the experiments for the DARTS sear

Simple Spectral Graph Convolutional Overview This repo contains an example implementation of the Simple Spectral Graph Convolutional (S^2GC) model. Th

Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing This repository contains pytorch source code for AAAI2020 oral paper: Grapy-ML

Factor graphs + nonlinear optimization in JAX

OEIS explorer This is a tool for exploring two different kinds of relationships between sequences in the OEIS: mentions (links) of other sequences on

This is the repository for The Python Workshop, published by Packt. It contains all the supporting project files necessary to work through the course from start to finish.

DeepFRI Deep functional residue identification Citing @article {Gligorijevic2019, author = {Gligorijevic, Vladimir and Renfrew, P. Douglas and Koscio

TensorFlow Decision Forests (TF-DF) is a collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models. The library is a collection of Keras models and supports classification, regression and ranking. TF-DF is a TensorFlow wrapper around the Yggdrasil Decision Forests C++ libraries. Models trained with TF-DF are compatible with Yggdrasil Decision Forests' models, and vice versa.

If the compilation is successful, the evaluator of cpp implementation will be called automatically. Otherwise, the evaluator of python implementation will be called.

DDAMS This is the pytorch code for our IJCAI 2021 paper Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization [Arxiv Pr

An Unsupervised Graph-based Toolbox for Fraud Detection Introduction: UGFraud is an unsupervised graph-based fraud detection toolbox that integrates s

When2com: Multi-Agent Perception via Communication Graph Grouping This is the PyTorch implementation of our paper: When2com: Multi-Agent Perception vi

Expressive Power of Invariant and Equivaraint Graph Neural Networks In this repository, we show how to use powerful GNN (2-FGNN) to solve a graph alig

GCA Source code for Graph Contrastive Learning with Adaptive Augmentation (WWW 2021) For example, to run GCA-Degree under WikiCS, execute: python trai

PythonRobotics Python codes for robotics algorithm. Table of Contents What is this? Requirements Documentation How to use Localization Extended Kalman

HiddenWave Embedding secret messages in wave audio file What is HiddenWave Hiddenwave is a python based program for simple audio steganography. You ca

Genea: A Genetic Algorithm in Python Genea is a Genetic Algorithm written in Python, for educational purposes. I started writing it for fun, while lea