963 Repositories
Python knowledge-graph-embeddings Libraries
Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" (NeurIPS'20)
IGNN Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" [paper] [supp] Prepare datasets 1 Download training dataset
Benchmark datasets, data loaders, and evaluators for graph machine learning
Overview The Open Graph Benchmark (OGB) is a collection of benchmark datasets, data loaders, and evaluators for graph machine learning. Datasets cover
Implementation of Graph Convolutional Networks in TensorFlow
Graph Convolutional Networks This is a TensorFlow implementation of Graph Convolutional Networks for the task of (semi-supervised) classification of n
official implementation for the paper "Simplifying Graph Convolutional Networks"
Simplifying Graph Convolutional Networks Updates As pointed out by #23, there was a subtle bug in our preprocessing code for the reddit dataset. After
PyTorch Implement for Path Attention Graph Network
SPAGAN in PyTorch This is a PyTorch implementation of the paper "SPAGAN: Shortest Path Graph Attention Network" Prerequisites We prefer to create a ne
Attention over nodes in Graph Neural Networks using PyTorch (NeurIPS 2019)
Intro This repository contains code to generate data and reproduce experiments from our NeurIPS 2019 paper: Boris Knyazev, Graham W. Taylor, Mohamed R
Graph Convolutional Networks for Temporal Action Localization (ICCV2019)
Graph Convolutional Networks for Temporal Action Localization This repo holds the codes and models for the PGCN framework presented on ICCV 2019 Graph
IsoGCN code for ICLR2021
IsoGCN The official implementation of IsoGCN, presented in the ICLR2021 paper Isometric Transformation Invariant and Equivariant Graph Convolutional N
PyTorch implementation of "Simple and Deep Graph Convolutional Networks"
Simple and Deep Graph Convolutional Networks This repository contains a PyTorch implementation of "Simple and Deep Graph Convolutional Networks".(http
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks This repository contains a TensorFlow implementation of "
[ICML 2020] "When Does Self-Supervision Help Graph Convolutional Networks?" by Yuning You, Tianlong Chen, Zhangyang Wang, Yang Shen
When Does Self-Supervision Help Graph Convolutional Networks? PyTorch implementation for When Does Self-Supervision Help Graph Convolutional Networks?
This is a Pytorch implementation of paper: DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
DropEdge: Towards Deep Graph Convolutional Networks on Node Classification This is a Pytorch implementation of paper: DropEdge: Towards Deep Graph Con
Repository for benchmarking graph neural networks
Benchmarking Graph Neural Networks Updates Nov 2, 2020 Project based on DGL 0.4.2. See the relevant dependencies defined in the environment yml files
Graph Attention Networks
GAT Graph Attention Networks (Veličković et al., ICLR 2018): https://arxiv.org/abs/1710.10903 GAT layer t-SNE + Attention coefficients on Cora Overvie
PyTorch implementation of residual gated graph ConvNets, ICLR’18
Residual Gated Graph ConvNets April 24, 2018 Xavier Bresson http://www.ntu.edu.sg/home/xbresson https://github.com/xbresson https://twitter.com/xbress
Tensorflow Repo for "DeepGCNs: Can GCNs Go as Deep as CNNs?"
DeepGCNs: Can GCNs Go as Deep as CNNs? In this work, we present new ways to successfully train very deep GCNs. We borrow concepts from CNNs, mainly re
PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training”
A new codebase for popular Scene Graph Generation methods (2020). Visualization & Scene Graph Extraction on custom images/datasets are provided. It's also a PyTorch implementation of paper “Unbiased Scene Graph Generation from Biased Training CVPR 2020”
Markov Chain Composer
Markov Chain Composer Using Markov Chain to represent relationships between words in song lyrics and then generating new lyrics.. ahem interpretive po
GraPE is a Rust/Python library for high-performance Graph Processing and Embedding.
GraPE GraPE (Graph Processing and Embedding) is a fast graph processing and embedding library, designed to scale with big graphs and to run on both of
This repository implements variational graph auto encoder by Thomas Kipf.
Variational Graph Auto-encoder in Pytorch This repository implements variational graph auto-encoder by Thomas Kipf. For details of the model, refer to
Code for the preprint "Well-classified Examples are Underestimated in Classification with Deep Neural Networks"
This is a repository for the paper of "Well-classified Examples are Underestimated in Classification with Deep Neural Networks" The implementation and
Semi-Supervised Signed Clustering Graph Neural Network (and Implementation of Some Spectral Methods)
SSSNET SSSNET: Semi-Supervised Signed Network Clustering For details, please read our paper. Environment Setup Overview The project has been tested on
Official implementation of "Motif-based Graph Self-Supervised Learning forMolecular Property Prediction"
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction Official Pytorch implementation of NeurIPS'21 paper "Motif-based Graph Se
A PoC Corporation Relationship Knowledge Graph System on top of Nebula Graph.
Corp-Rel is a PoC of Corpartion Relationship Knowledge Graph System. It's built on top of the Open Source Graph Database: Nebula Graph with a dataset
Code for "Understanding Pooling in Graph Neural Networks"
Select, Reduce, Connect This repository contains the code used for the experiments of: "Understanding Pooling in Graph Neural Networks" Setup Install
Official implementation for Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos
Multi-modal Interaction Graph Convolutioal Network for Temporal Language Localization in Videos Official implementation for Multi-Modal Interaction Gr
A brand new hub for Scene Graph Generation methods based on MMdetection (2021). The pipeline of from detection, scene graph generation to downstream tasks (e.g., image cpationing) is supported. Pytorch version implementation of HetH (ECCV 2020) and TopicSG (ICCV 2021) is included.
MMSceneGraph Introduction MMSceneneGraph is an open source code hub for scene graph generation as well as supporting downstream tasks based on the sce
Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.
3D Infomax improves GNNs for Molecular Property Prediction Video | Paper We pre-train GNNs to understand the geometry of molecules given only their 2D
Code for Understanding Pooling in Graph Neural Networks
Select, Reduce, Connect This repository contains the code used for the experiments of: "Understanding Pooling in Graph Neural Networks" Setup Install
Official PyTorch implementation of "AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks"
AASIST This repository provides the overall framework for training and evaluating audio anti-spoofing systems proposed in 'AASIST: Audio Anti-Spoofing
Deep Structured Instance Graph for Distilling Object Detectors (ICCV 2021)
DSIG Deep Structured Instance Graph for Distilling Object Detectors Authors: Yixin Chen, Pengguang Chen, Shu Liu, Liwei Wang, Jiaya Jia. [pdf] [slide]
A framework for building (and incrementally growing) graph-based data structures used in hierarchical or DAG-structured clustering and nearest neighbor search
A framework for building (and incrementally growing) graph-based data structures used in hierarchical or DAG-structured clustering and nearest neighbor search
Permute Me Softly: Learning Soft Permutations for Graph Representations
Permute Me Softly: Learning Soft Permutations for Graph Representations
BPEmb is a collection of pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE) and trained on Wikipedia.
BPEmb is a collection of pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE) and trained on Wikipedia. Its intended use is as input for neural models in natural language processing.
Code for the paper Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations (AKBC 2021).
Relation Prediction as an Auxiliary Training Objective for Knowledge Base Completion This repo provides the code for the paper Relation Prediction as
A graph neural network (GNN) model to predict protein-protein interactions (PPI) with no sample features
A graph neural network (GNN) model to predict protein-protein interactions (PPI) with no sample features
A geometric deep learning pipeline for predicting protein interface contacts.
A geometric deep learning pipeline for predicting protein interface contacts.
An easy-to-use Python module that helps you to extract the BERT embeddings for a large text dataset (Bengali/English) efficiently.
An easy-to-use Python module that helps you to extract the BERT embeddings for a large text dataset (Bengali/English) efficiently.
A framework for evaluating Knowledge Graph Embedding Models in a fine-grained manner.
A framework for evaluating Knowledge Graph Embedding Models in a fine-grained manner.
GNNLens2 is an interactive visualization tool for graph neural networks (GNN).
GNNLens2 is an interactive visualization tool for graph neural networks (GNN).
DGCNN - Dynamic Graph CNN for Learning on Point Clouds
DGCNN is the author's re-implementation of Dynamic Graph CNN, which achieves state-of-the-art performance on point-cloud-related high-level tasks including category classification, semantic segmentation and part segmentation.
This is the official pytorch implementation of Student Helping Teacher: Teacher Evolution via Self-Knowledge Distillation(TESKD)
Student Helping Teacher: Teacher Evolution via Self-Knowledge Distillation (TESKD) By Zheng Li[1,4], Xiang Li[2], Lingfeng Yang[2,4], Jian Yang[2], Zh
The source code and data of the paper "Instance-wise Graph-based Framework for Multivariate Time Series Forecasting".
IGMTF The source code and data of the paper "Instance-wise Graph-based Framework for Multivariate Time Series Forecasting". Requirements The framework
RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering
RNG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering Authors: Xi Ye, Semih Yavuz, Kazuma Hashimoto, Yingbo Zhou and
Large-scale open domain KNOwledge grounded conVERsation system based on PaddlePaddle
Knover Knover is a toolkit for knowledge grounded dialogue generation based on PaddlePaddle. Knover allows researchers and developers to carry out eff
Implementation of Neural Distance Embeddings for Biological Sequences (NeuroSEED) in PyTorch
Neural Distance Embeddings for Biological Sequences Official implementation of Neural Distance Embeddings for Biological Sequences (NeuroSEED) in PyTo
GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition
GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition
This is the source code for: Context-aware Entity Typing in Knowledge Graphs.
This is the source code for: Context-aware Entity Typing in Knowledge Graphs.
A Pytorch implementation of "Splitter: Learning Node Representations that Capture Multiple Social Contexts" (WWW 2019).
Splitter ⠀⠀ A PyTorch implementation of Splitter: Learning Node Representations that Capture Multiple Social Contexts (WWW 2019). Abstract Recent inte
A Structured Self-attentive Sentence Embedding
Structured Self-attentive sentence embeddings Implementation for the paper A Structured Self-Attentive Sentence Embedding, which was published in ICLR
Convolutional 2D Knowledge Graph Embeddings resources
ConvE Convolutional 2D Knowledge Graph Embeddings resources. Paper: Convolutional 2D Knowledge Graph Embeddings Used in the paper, but do not use thes
PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"
Poincaré Embeddings for Learning Hierarchical Representations PyTorch implementation of Poincaré Embeddings for Learning Hierarchical Representations
Apply Graph Self-Supervised Learning methods to graph-level task(TUDataset, MolculeNet Datset)
Graphlevel-SSL Overview Apply Graph Self-Supervised Learning methods to graph-level task(TUDataset, MolculeNet Dataset). It is unified framework to co
Dynamic Attentive Graph Learning for Image Restoration, ICCV2021 [PyTorch Code]
Dynamic Attentive Graph Learning for Image Restoration This repository is for GATIR introduced in the following paper: Chong Mou, Jian Zhang, Zhuoyuan
Original implementation of the pooling method introduced in "Speaker embeddings by modeling channel-wise correlations"
Speaker-Embeddings-Correlation-Pooling This is the original implementation of the pooling method introduced in "Speaker embeddings by modeling channel
Improving Convolutional Networks via Attention Transfer (ICLR 2017)
Attention Transfer PyTorch code for "Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Tran
Official DGL implementation of "Rethinking High-order Graph Convolutional Networks"
SE Aggregation This is the implementation for Rethinking High-order Graph Convolutional Networks. Here we show the codes for citation networks as an e
Applying "Load What You Need: Smaller Versions of Multilingual BERT" to LaBSE
smaller-LaBSE LaBSE(Language-agnostic BERT Sentence Embedding) is a very good method to get sentence embeddings across languages. But it is hard to fi
EMNLP'2021: Can Language Models be Biomedical Knowledge Bases?
BioLAMA BioLAMA is biomedical factual knowledge triples for probing biomedical LMs. The triples are collected and pre-processed from three sources: CT
Does Pretraining for Summarization Reuqire Knowledge Transfer?
Pretraining summarization models using a corpus of nonsense
Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective Knowledge. Proceedings of EMNLP 2021
AAGCN-ACSA EMNLP 2021 Introduction This repository was used in our paper: Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment An
Pyan3 - Offline call graph generator for Python 3
Pyan takes one or more Python source files, performs a (rather superficial) static analysis, and constructs a directed graph of the objects in the combined source, and how they define or use each other. The graph can be output for rendering by GraphViz or yEd.
Obsidian tools - a Python package for analysing an Obsidian.md vault
obsidiantools is a Python package for getting structured metadata about your Obsidian.md notes and analysing your vault.
Official PyTorch code of DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization (ICCV 2021 Oral).
DeepPanoContext (DPC) [Project Page (with interactive results)][Paper] DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context G
Embeddinghub is a database built for machine learning embeddings.
Embeddinghub is a database built for machine learning embeddings.
This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL [Deep Graph Library] and PyTorch.
This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL [Deep Graph Library] and PyTorch.
Code for the paper in Findings of EMNLP 2021: "EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation".
This repository contains the code for the paper in Findings of EMNLP 2021: "EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation".
ROSITA: Enhancing Vision-and-Language Semantic Alignments via Cross- and Intra-modal Knowledge Integration
ROSITA News & Updates (24/08/2021) Release the demo to perform fine-grained semantic alignments using the pretrained ROSITA model. (15/08/2021) Releas
[ICCV2021] Official code for "Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition"
CTR-GCN This repo is the official implementation for Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition. The pap
MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks.
MVGCN MVGCN: a novel multi-view graph convolutional network (MVGCN) framework for link prediction in biomedical bipartite networks. Developer: Fu Hait
This repository contains the PyTorch implementation of the paper STaCK: Sentence Ordering with Temporal Commonsense Knowledge appearing at EMNLP 2021.
STaCK: Sentence Ordering with Temporal Commonsense Knowledge This repository contains the pytorch implementation of the paper STaCK: Sentence Ordering
Adaptive Graph Convolution for Point Cloud Analysis
Adaptive Graph Convolution for Point Cloud Analysis This repository contains the implementation of AdaptConv for point cloud analysis. Adaptive Graph
Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting (ICCV, 2021)
DKPNet ICCV 2021 Variational Attention: Propagating Domain-Specific Knowledge for Multi-Domain Learning in Crowd Counting Baseline of DKPNet is availa
A fluid medium for storing, relating, and surfacing thoughts.
Conceptarium A fluid medium for storing, relating, and surfacing thoughts. Read more... Instructions The conceptarium takes up about 1GB RAM when runn
Repository for Graph2Pix: A Graph-Based Image to Image Translation Framework
Graph2Pix: A Graph-Based Image to Image Translation Framework Installation Install the dependencies in env.yml $ conda env create -f env.yml $ conda a
G-NIA model from "Single Node Injection Attack against Graph Neural Networks" (CIKM 2021)
Single Node Injection Attack against Graph Neural Networks This repository is our Pytorch implementation of our paper: Single Node Injection Attack ag
A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning", CIKM-21
ANEMONE A PyTorch implementation of "ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning", CIKM-21 Dependencies python==3.6.1 dgl==
yolov5目标检测模型的知识蒸馏(基于响应的蒸馏)
代码地址: https://github.com/Sharpiless/yolov5-knowledge-distillation 教师模型: python train.py --weights weights/yolov5m.pt \ --cfg models/yolov5m.ya
Galileo library for large scale graph training by JD
近年来,图计算在搜索、推荐和风控等场景中获得显著的效果,但也面临超大规模异构图训练,与现有的深度学习框架Tensorflow和PyTorch结合等难题。 Galileo(伽利略)是一个图深度学习框架,具备超大规模、易使用、易扩展、高性能、双后端等优点,旨在解决超大规模图算法在工业级场景的落地难题,提
🍊 PAUSE (Positive and Annealed Unlabeled Sentence Embedding), accepted by EMNLP'2021 🌴
PAUSE: Positive and Annealed Unlabeled Sentence Embedding Sentence embedding refers to a set of effective and versatile techniques for converting raw
✨Rubrix is a production-ready Python framework for exploring, annotating, and managing data in NLP projects.
✨A Python framework to explore, label, and monitor data for NLP projects
Implementation for the EMNLP 2021 paper "Interactive Machine Comprehension with Dynamic Knowledge Graphs".
Interactive Machine Comprehension with Dynamic Knowledge Graphs Implementation for the EMNLP 2021 paper. Dependencies apt-get -y update apt-get instal
PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation
PocketNet This is the official repository of the paper: PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and M
ZSL-KG is a general-purpose zero-shot learning framework with a novel transformer graph convolutional network (TrGCN) to learn class representation from common sense knowledge graphs.
ZSL-KG is a general-purpose zero-shot learning framework with a novel transformer graph convolutional network (TrGCN) to learn class representa
Open Source Tool - Cybersecurity Graph Database in Neo4j
GraphKer Open Source Tool - Cybersecurity Graph Database in Neo4j |G|r|a|p|h|K|e|r| { open source tool for a cybersecurity graph database in neo4j } W
Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'.
COTREC Codes for CIKM'21 paper 'Self-Supervised Graph Co-Training for Session-based Recommendation'. Requirements: Python 3.7, Pytorch 1.6.0 Best Hype
MWPToolkit is a PyTorch-based toolkit for Math Word Problem (MWP) solving.
MWPToolkit is a PyTorch-based toolkit for Math Word Problem (MWP) solving. It is a comprehensive framework for research purpose that integrates popular MWP benchmark datasets and typical deep learning-based MWP algorithms.
Poplar implementation of "Bundle Adjustment on a Graph Processor" (CVPR 2020)
Poplar Implementation of Bundle Adjustment using Gaussian Belief Propagation on Graphcore's IPU Implementation of CVPR 2020 paper: Bundle Adjustment o
This repo uses a combination of logits and feature distillation method to teach the PSPNet model of ResNet18 backbone with the PSPNet model of ResNet50 backbone. All the models are trained and tested on the PASCAL-VOC2012 dataset.
PSPNet-logits and feature-distillation Introduction This repository is based on PSPNet and modified from semseg and Pixelwise_Knowledge_Distillation_P
🌈 PyTorch Implementation for EMNLP'21 Findings "Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer"
SGLKT-VisDial Pytorch Implementation for the paper: Reasoning Visual Dialog with Sparse Graph Learning and Knowledge Transfer Gi-Cheon Kang, Junseok P
GraphGT: Machine Learning Datasets for Graph Generation and Transformation
GraphGT: Machine Learning Datasets for Graph Generation and Transformation Dataset Website | Paper Installation Using pip To install the core environm
Differentiable Factor Graph Optimization for Learning Smoothers @ IROS 2021
Differentiable Factor Graph Optimization for Learning Smoothers Overview Status Setup Datasets Training Evaluation Acknowledgements Overview Code rele
TorchDistiller - a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.
This project is a collection of the open source pytorch code for knowledge distillation, especially for the perception tasks, including semantic segmentation, depth estimation, object detection and instance segmentation.
DomainWordsDict, Chinese words dict that contains more than 68 domains, which can be used as text classification、knowledge enhance task
DomainWordsDict, Chinese words dict that contains more than 68 domains, which can be used as text classification、knowledge enhance task。涵盖68个领域、共计916万词的专业词典知识库,可用于文本分类、知识增强、领域词汇库扩充等自然语言处理应用。
PyTorch Code for the paper "VSE++: Improving Visual-Semantic Embeddings with Hard Negatives"
Improving Visual-Semantic Embeddings with Hard Negatives Code for the image-caption retrieval methods from VSE++: Improving Visual-Semantic Embeddings
Graph-based community clustering approach to extract protein domains from a predicted aligned error matrix
Using a predicted aligned error matrix corresponding to an AlphaFold2 model , returns a series of lists of residue indices, where each list corresponds to a set of residues clustering together into a pseudo-rigid domain.
[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang
Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study Codes for [Preprint] Bag of Tricks for Training Deeper Graph
Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'
Filtration Curves for Graph Representation This repository provides the code from the KDD'21 paper Filtration Curves for Graph Representation. Depende
A PyTorch implementation of "Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning", IJCAI-21
MERIT A PyTorch implementation of our IJCAI-21 paper Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning. Depen
Local trajectory planner based on a multilayer graph framework for autonomous race vehicles.
Graph-Based Local Trajectory Planner The graph-based local trajectory planner is python-based and comes with open interfaces as well as debug, visuali