Python Graph-libraries Resources

SPTAG: A library for fast approximate nearest neighbor search SPTAG SPTAG (Space Partition Tree And Graph) is a library for large scale vector approxi

This repository holds the implementation for paper Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach Download our preproc

Parameterized Hypercomplex Graph Neural Networks (PHC-GNNs) PHC-GNNs (Le et al., 2021): https://arxiv.org/abs/2103.16584 PHM Linear Layer Illustration

SSR (NeurIPS 2021) Pytorch implementation of paper "Re-ranking for image retrieval and transductivefew-shot classification" [Paper] [Project webpage]

SAGCN - Official PyTorch Implementation | Paper | Project Page This is the official implementation of the paper "Steganographer detection via a simila

g2o - General Graph Optimization Linux: Windows: g2o is an open-source C++ framework for optimizing graph-based nonlinear error functions. g2o has bee

ADGC: Awesome Deep Graph Clustering ADGC is a collection of state-of-the-art (SOTA), novel deep graph clustering methods (papers, codes and datasets).

Orbitals in Python A small collection of tools made by me, that you can use to visualize atomic orbitals in both 2D and 3D in different aspects, and o

SURGE: Sequential Recommendation with Graph Neural Networks This is our TensorFlow implementation for the paper: Sequential Recommendation with Graph

MassFormer This is the original implementation of MassFormer, a graph transformer for small molecule MS/MS prediction. Check out the preprint on arxiv

ChebLieNet: Invariant spectral graph NNs turned equivariant by Riemannian geometry on Lie groups Hugo Aguettaz, Erik J. Bekkers, Michaël Defferrard We

Dynamic-Graphs-Construction Official Codes for Graph Modularity:Towards Understanding the Cross-Layer Transition of Feature Representations in Deep Ne

2s-AGCN Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition in CVPR19 Note PyTorch version should be 0.3! For PyTor

Fine-grained Sentiment Analysis as Dependency Graph Parsing This repository contains the code and datasets described in following paper: Fine-grained

SwisE Switch spaces for knowledge graph embeddings. Requirements: python3 pytorch numpy tqdm Reproduce the results To reproduce the reported results,

Graph Convolutional Gated Recurrent Neural Network (GCGRNN) Improved from Graph Convolutional Neural Networks with Data-driven Graph Filter (GCNN-DDGF

ENN Incubator Collection of in-progress libraries for entity neural networks: Neural Network Architectures for Structured State Entity Gym: Abstractio

Binomial Option Pricing Calculator Option Pricing Calculator using the Binomial Pricing Method (No Libraries Required) Background A derivative is a fi

Overview of OpenPNM OpenPNM is a comprehensive framework for performing pore network simulations of porous materials. More Information For more detail

WhiteSource Top 10 Rejected Libraries Generate a spreadsheet listing the 10 most common libraries in your WhiteSource inventory that were rejected by

Pigment The problem of partitioning up a skincare regime into parts such that each part does not interfere with itself is equivalent to the minimal cl

Introduction ImagePy is an open source image processing framework written in Python. Its UI interface, image data structure and table data structure a

Code has been run on Google Colab, thanks Google for providing computational resources Contents Natural Language Processing（自然语言处理） Text Classificati

Welcome to Avocado Avocado is a set of tools and libraries to help with automated testing. One can call it a test framework with benefits. Native test

Ohm-s-Law-Visualizer It's an application to calculate I from v and r using Ohm's Law. It can also plot a graph between V vs I. Story I'm doing my Unde

Implementing Graph Convolutional Networks and Information Retrieval Mechanisms using pure Python and NumPy

GN-Transformer AST This is the official repository for the paper "GN-Transformer: Fusing AST and Source Code information in Graph Networks". Data Prep

English | 简体中文 Latest News 2021.10.25 Paper "Docking-based Virtual Screening with Multi-Task Learning" is accepted by BIBM 2021. 2021.07.29 PaddleHeli

GemNet: Universal Directional Graph Neural Networks for Molecules Reference implementation in PyTorch of the geometric message passing neural network

dm_robotics: Libraries, tools, and tasks created and used for Robotics research at DeepMind. Package overview Package Summary Transformations Rigid bo

TensorFlow GNN This is an early (alpha) release to get community feedback. It's under active development and we may break API compatibility in the fut

Some tentative models that incorporate label propagation to graph neural networks for graph representation learning in nodes, links or graphs.

Housegan-data-reader House-GAN++ (data-reader) Code and instructions for converting rplan dataset (raster images) to housegan++ data format. House-GAN

Graph Nets library Graph Nets is DeepMind's library for building graph networks in Tensorflow and Sonnet. Contact [email protected] for comments a

CG3 This is the repository for the AAAI 21 paper [Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning]. R

Time2box Implementation of [Time in a Box: Advancing Knowledge Graph Completion with Temporal Scopes].

HaloGraphNet Predict halo masses from simulations via Graph Neural Networks. Given a dark matter halo and its galaxies, creates a graph with informati

Interpreting Language Models Through Knowledge Graph Extraction Idea: How do we interpret what a language model learns at various stages of training?

EZ-Graph EZ Graph is a GUI that allows users to make and train neural networks without writing a single line of code. Requirements python 3 pandas num

GATER This repository contains the code for our EMNLP 2021 paper “Heterogeneous Graph Neural Networks for Keyphrase Generation”. Our implementation is

GraphWar: Arms Race in Graph Adversarial Learning NOTE: GraphWar is still in the early stages and the API will likely continue to change. 🚀 Installat

A criticism of a recent paper on buggy image downsampling methods in popular image processing and deep learning libraries.

This is a repository of libraries designed to be useful for writing MicroPython applications.

GOAT A paper using optimal transport to solve the graph matching problem. https://arxiv.org/abs/2111.05366 Repo structure .github: Files specifying ho

isvd Official implementation of NeurIPS'21: Implicit SVD for Graph Representation Learning If you find this code useful, you may cite us as: @inprocee

Climate Modeling with Neural Diffusion Equation Introduction This is the repository of our accepted ICDM 2021 paper "Climate Modeling with Neural Diff

Python Standard Libraries Cheatsheet Depend on Python v3.9.8 All code snippets have been tested to ensure they work properly. Fork me on GitHub. 中文 En

SGTL - Spectral Graph Theory Library SGTL is a python library of spectral graph theory methods. The library is still very new and so there are many fe

ICS-Visualizer is an interactive Industrial Control Systems (ICS) network graph that contains up-to-date ICS metadata (Name, company, port, user manua

Overview Metrinome is an all-purpose tool for working with code complexity metrics. It can be used as both a REPL and API, and includes: Converters to

Py2neo Py2neo is a client library and toolkit for working with Neo4j from within Python applications. The library supports both Bolt and HTTP and prov

Yolo-Powered-Detector A object detecting neural network powered by the yolo architecture and leveraging the PyTorch framework and associated libraries

Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods Introduction Graph Neural Networks (GNNs) have demonstrated

MassFormer This is the original implementation of MassFormer, a graph transformer for small molecule MS/MS prediction. Check out the preprint on arxiv

Homepage | Paper | Datasets | Leaderboard | Documentation Graph Robustness Benchmark (GRB) provides scalable, unified, modular, and reproducible evalu

Polypharmacy - DDI - Synergy Survey The Survey Paper This repository accompanies our survey paper A Unified View of Relational Deep Learning for Polyp

GDN A tensorflow=1.13 implementation of Deconvolutional Networks on Graph Data (NeurIPS 2021) Abstract In this paper, we consider an inverse problem i

POS-Tagger This repository details the creation of a Part-of-Speech tagger using Trigram Hidden Markov Models to predict word tags in a word sequence.

SGMNet Implementation PyTorch implementation of SGMNet for ICCV'21 paper "Learning to Match Features with Seeded Graph Matching Network", by Hongkai C

Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods This is the code repository to accompany the EMNLP 2021 paper on ad

About Unsub is a collection analysis tool that assists libraries in analyzing their journal subscriptions. The tool provides rich data and a summary g

refcount master: testing: This package is primarily for managing resources in native libraries, written for instance in C++, from Python. While it boi

CaGCN This repo is for source code of NeurIPS 2021 paper "Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration". Paper L

Polypharmacy - DDI - Synergy Survey The Survey Paper This repository accompanies our survey paper A Unified View of Relational Deep Learning for Polyp

ADGCL : Adversarial Graph Augmentation to Improve Graph Contrastive Learning Introduction This repo contains the Pytorch [1] implementation of Adversa

Learning Graph Cellular Automata This code implements the experiments from the NeurIPS 2021 paper: "Learning Graph Cellular Automata" Daniele Grattaro

Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback This is our Pytorch implementation for the paper: Yinwei Wei,

RHGN Source code for CIKM 2021 paper for Relation-aware Heterogeneous Graph for User Profiling Dependencies torch==1.6.0 torchvision==0.7.0 dgl==0.7.1

Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods Introduction Graph Neural Networks (GNNs) have demonstrated

Edge Independent Graph Models Code for the paper: "On the Power of Edge Independent Graph Models" Sudhanshu Chanpuriya, Cameron Musco, Konstantinos So

IGCN : Image-to-graph convolutional network IGCN is a learning framework for 2D/3D deformable model registration and alignment, and shape reconstructi

RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule Mining Our code is based on Learning Attention-based Embed

Data and Code for paper Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge Graph is available f

DSP: Trajectory Prediction with Graph-based Dual-scale Context Fusion Introduction This is the project page of the paper Lu Zhang, Peiliang Li, Jing C

Diverse Motion Stylization (Official) This is the official Pytorch implementation of this paper. Diverse Motion Stylization for Multiple Style Domains

DiscoNet: Learning Distilled Collaboration Graph for Multi-Agent Perception [NeurIPS 2021] Yiming Li, Shunli Ren, Pengxiang Wu, Siheng Chen, Chen Feng

A large-scale database for graph representation learning

UltraGCN This is our Pytorch implementation for our CIKM 2021 paper: Kelong Mao, Jieming Zhu, Xi Xiao, Biao Lu, Zhaowei Wang, Xiuqiang He. UltraGCN: A

GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily Abstract Graph Neural Networks (GNNs) are widely used on a

Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach This is the implementation of traffic prediction code in DTMP based on PyTo

stock-graph-python Simple GUI python app to show a stocks graph performance. Made with Matplotlib and Tiingo. Tiingo API Key You will need to add your

Fast and Easy-to-use Distributed Graph Learning for PyTorch Geometric

Ansible Playbook Grapher ansible-playbook-grapher is a command line tool to create a graph representing your Ansible playbook plays, tasks and roles.

Graph Regularized Residual Subspace Clustering Network for hyperspectral image clustering

Heterogeneous Temporal Graph Neural Network This repository contains the datasets and source code of HTGNN. run_mag.ipynb is the training and testing

Learning Graph Cellular Automata This code implements the experiments from the NeurIPS 2021 paper: "Learning Graph Cellular Automata" Daniele Grattaro

BMVC 2021 BI-GConv: Boundary-Aware Input-Dependent Graph Convolution for Biomedical Image Segmentation Necassary Dependencies: PyTorch 1.2.0 Python 3.

VACA Code repository for the paper "VACA: Designing Variational Graph Autoencoders for Interventional and Counterfactual Queries (arXiv)". The impleme

Rot-Pro : Modeling Transitivity by Projection in Knowledge Graph Embedding This repository contains the source code for the Rot-Pro model, presented a

Node Dependent Local Smoothing for Scalable Graph Learning Requirements Environments: Xeon Gold 5120 (CPU), 384GB(RAM), TITAN RTX (GPU), Ubuntu 16.04

SparseCard Code for approximate graph reduction techniques for cardinality-based DSFM, from paper "Approximate Decomposable Submodular Function Minimi

Implementation for On Provable Benefits of Depth in Training Graph Convolutional Networks Setup This implementation is based on PyTorch = 1.0.0. Smal

SMORE: Knowledge Graph Completion and Multi-hop Reasoning in Massive Knowledge Graphs SMORE is a a versatile framework that scales multi-hop query emb

Hierarchical User Intent Graph Network for Multimedia Recommendation This is our Pytorch implementation for the paper: Hierarchical User Intent Graph

A suite of benchmarks for CPU and GPU performance of the most popular high-performance libraries for Python :rocket:

Video Matting via Consistency-Regularized Graph Neural Networks Project Page | Real Data | Paper Installation Our code has been tested on Python 3.7,

Albumentations Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to inc

Predicting lncRNA–protein interactions based on graph autoencoders and collaborative training Code for our paper "Predicting lncRNA–protein interactio

Nested Graph Neural Networks About Nested Graph Neural Network (NGNN) is a general framework to improve a base GNN's expressive power and performance.

Exponential Graph is Provably Efficient for Decentralized Deep Training This code repository is for the paper Exponential Graph is Provably Efficient