From Canonical Correlation Analysis to Self-supervised Graph Neural Networks

Related tags

Deep LearningCCA-SSG
Overview

[NeurIPS 2021]-From Canonical Correlation Analysis to Self-supervised Graph Neural Networks

Code for CCA-SSG model proposed in the NeurIPS 2021 paper From Canonical Correlation Analysis to Self-supervised Graph Neural Networks.

Dependencies

  • Python 3.7
  • PyTorch 1.7.1
  • dgl 0.6.0

Datasets

Citation Networks: 'Cora', 'Citeseer' and 'Pubmed'.

Co-occurence Networks: 'Amazon-Computer', 'Amazon-Photo', 'Coauthor-CS' and 'Coauthor-Physics'.

Dataset # Nodes # Edges # Classes # Features
Cora 2,708 10,556 7 1,433
Citeseer 3,327 9,228 6 3,703
Pubmed 19,717 88,651 3 500
Amazon-Computer 13,752 574,418 10 767
Amazon-Photo 7,650 287,326 8 745
Coauthor-CS 18,333 327,576 15 6,805
Coauthor-Physics 34,493 991,848 5 8,451

Usage

To run the codes, use the following commands:

# Cora
python main.py --dataname cora --epochs 50 --lambd 1e-3 --dfr 0.1 --der 0.4 --lr2 1e-2 --wd2 1e-4

# Citeseer
python main.py --dataname citeseer --epochs 20 --n_layers 1 --lambd 5e-4 --dfr 0.0 --der 0.4 --lr2 1e-2 --wd2 1e-2

# Pubmed
python main.py --dataname pubmed --epochs 100 --lambd 1e-3 --dfr 0.3 --der 0.5 --lr2 1e-2 --wd2 1e-4

# Amazon-Computer
python main.py --dataname comp --epochs 50 --lambd 5e-4 --dfr 0.1 --der 0.3 --lr2 1e-2 --wd2 1e-4

# Amazon-Photo
python main.py --dataname photo --epochs 50 --lambd 1e-3 --dfr 0.2 --der 0.3 --lr2 1e-2 --wd2 1e-4

# Coauthor-CS
python main.py --dataname cs --epochs 50 --lambd 1e-3 --dfr 0.2 --lr2 5e-3 --wd2 1e-4 --use_mlp

# Coauthor-Physics
python main.py --dataname physics --epochs 100 --lambd 1e-3 --dfr 0.5 --der 0.5 --lr2 5e-3 --wd2 1e-4

Reference

If our paper and code are useful for your research, please cite the following article:

@inproceedings{zhang2021canonical,
  title={From canonical correlation analysis to self-supervised graph neural networks},
  author={Zhang, Hengrui and Wu, Qitian and Yan, Junchi and Wipf, David and Philip, S Yu},
  booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
  year={2021}
}
Owner
Hengrui Zhang
Hengrui Zhang
PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis

Daft-Exprt - PyTorch Implementation PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis The

Keon Lee 47 Dec 18, 2022
Template repository for managing machine learning research projects built with PyTorch-Lightning

Tutorial Repository with a minimal example for showing how to deploy training across various compute infrastructure.

Sidd Karamcheti 3 Feb 11, 2022
gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions

gtfs2vec This is a companion repository for a gtfs2vec - Learning GTFS Embeddings for comparing PublicTransport Offer in Microregions publication. Vis

Politechnika Wrocławska - repozytorium dla informatyków 5 Oct 10, 2022
Codebase for INVASE: Instance-wise Variable Selection - 2019 ICLR

Codebase for "INVASE: Instance-wise Variable Selection" Authors: Jinsung Yoon, James Jordon, Mihaela van der Schaar Paper: Jinsung Yoon, James Jordon,

Jinsung Yoon 50 Nov 11, 2022
​ This is the Pytorch implementation of Progressive Attentional Manifold Alignment.

PAMA This is the Pytorch implementation of Progressive Attentional Manifold Alignment. Requirements python 3.6 pytorch 1.2.0+ PIL, numpy, matplotlib C

98 Nov 15, 2022
Open source Python module for computer vision

About PCV PCV is a pure Python library for computer vision based on the book "Programming Computer Vision with Python" by Jan Erik Solem. More details

Jan Erik Solem 1.9k Jan 06, 2023
TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

TensorFlow implementation of "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

YeongHyeon Park 7 Aug 28, 2022
Creating a custom CNN hypertunned architeture for the Fashion MNIST dataset with Python, Keras and Tensorflow.

custom-cnn-fashion-mnist Creating a custom CNN hypertunned architeture for the Fashion MNIST dataset with Python, Keras and Tensorflow. The following

Danielle Almeida 1 Mar 05, 2022
PyTorch code for the paper "Complementarity is the King: Multi-modal and Multi-grained Hierarchical Semantic Enhancement Network for Cross-modal Retrieval".

Complementarity is the King: Multi-modal and Multi-grained Hierarchical Semantic Enhancement Network for Cross-modal Retrieval (M2HSE) PyTorch code fo

Xinlei-Pei 6 Dec 23, 2022
Pytorch implementation of the paper DocEnTr: An End-to-End Document Image Enhancement Transformer.

DocEnTR Description Pytorch implementation of the paper DocEnTr: An End-to-End Document Image Enhancement Transformer. This model is implemented on to

Mohamed Ali Souibgui 74 Jan 07, 2023
Deeper DCGAN with AE stabilization

AEGeAN Deeper DCGAN with AE stabilization Parallel training of generative adversarial network as an autoencoder with dedicated losses for each stage.

Tyler Kvochick 36 Feb 17, 2022
Create Data & AI apps in 20 lines of code with Shimoku

Install with: pip install shimoku-api-python Start with: from os import getenv import shimoku_api_python.client as Shimoku

Shimoku 5 Nov 07, 2022
Memory Defense: More Robust Classificationvia a Memory-Masking Autoencoder

Memory Defense: More Robust Classificationvia a Memory-Masking Autoencoder Authors: - Eashan Adhikarla - Dan Luo - Dr. Brian D. Davison Abstract Many

Eashan Adhikarla 4 Dec 25, 2022
Predicting Price of house by considering ,house age, Distance from public transport

House-Price-Prediction Predicting Price of house by considering ,house age, Distance from public transport, No of convenient stores around house etc..

Musab Jaleel 1 Jan 08, 2022
The Simplest DCGAN Implementation

DCGAN in TensorLayer This is the TensorLayer implementation of Deep Convolutional Generative Adversarial Networks. Looking for Text to Image Synthesis

TensorLayer Community 310 Dec 13, 2022
A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains (IJCV submission)

wsss-analysis The code of: A Comprehensive Analysis of Weakly-Supervised Semantic Segmentation in Different Image Domains, arXiv pre-print 2019 paper.

Lyndon Chan 48 Dec 18, 2022
Fake videos detection by tracing the source using video hashing retrieval.

Vision Transformer Based Video Hashing Retrieval for Tracing the Source of Fake Videos 🎉️ 📜 Directory Introduction VTL Trace Samples and Acc of Hash

56 Dec 22, 2022
A tensorflow implementation of an HMM layer

tensorflow_hmm Tensorflow and numpy implementations of the HMM viterbi and forward/backward algorithms. See Keras example for an example of how to use

Zach Dwiel 283 Oct 19, 2022
[CVPR'21] Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation

Projecting Your View Attentively: Monocular Road Scene Layout Estimation via Cross-view Transformation Weixiang Yang, Qi Li, Wenxi Liu, Yuanlong Yu, Y

118 Dec 26, 2022
PyBrain - Another Python Machine Learning Library.

PyBrain -- the Python Machine Learning Library =============================================== INSTALLATION ------------ Quick answer: make sure you

2.8k Dec 31, 2022