Code for the paper "Asymptotics of ℓ2 Regularized Network Embeddings"

Related tags

Deep Learningembed-reg
Overview

README

Code for the paper Asymptotics of L2 Regularized Network Embeddings.

Requirements

Requires Stellargraph 1.2.1, Tensorflow 2.6.0, scikit-learm 0.24.1, tqdm, along with any other packages required for the above three packages.

Code

To run node classification or link prediction experiments, run

python -m code.train_embed [[args]]

or

python -m code.train_embed_link [[args]]

from the command line respectively, where [[args]] correspond to the command line arguments for each function. Note that the scripts expect to run from the parent directory of the code folder; you will need to change the import statements in the associated python files if you move them around. The -h command line argument will display the arguments (with descriptions) of each of the two files.

train_embed.py arguments

short long default help
-h --help show this help message and exit
--dataset Cora Dataset to perform training on. Available options: Cora,CiteSeer,PubMedDiabetes
--emb-size 128 Embedding dimension. Defaults to 128.
--reg-weight 0.0 Weight to use for L2 regularization. If norm_reg is True, then reg_weight/num_of_nodes is used instead.
--norm-reg Boolean for whether to normalize the L2 regularization weight by the number of nodes in the graph. Defaults to false.
--method node2vec Algorithm to perform training on. Available options: node2vec,GraphSAGE,GCN,DGI
--verbose 1 Level of verbosity. Defaults to 1.
--epochs 5 Number of epochs through the dataset to be used for training.
--optimizer Adam Optimization algorithm to use for training.
--learning-rate 0.001 Learning rate to use for optimization.
--batch-size 64 Batch size used for training.
--train-split [0.01, 0.025, 0.05] Percentage(s) to use for the training split when using the learned embeddings for downstream classification tasks.
--train-split-num 25 Decides the number of random training/test splits to use for evaluating performance. Defaults to 50.
--output-fname None If not None, saves the hyperparameters and testing results to a .json file with filename given by the argument.
--node2vec-p 1.0 Hyperparameter governing probability of returning to source node.
--node2vec-q 1.0 Hyperparameter governing probability of moving to a node away from the source node.
--node2vec-walk-number 50 Number of walks used to generate a sample for node2vec.
--node2vec-walk-length 5 Walk length to use for node2vec.
--dgi-sampler fullbatch Specifies either a fullbatch or a minibatch sampling scheme for DGI.
--gcn-activation ['relu'] Determines the activations of each layer within a GCN. Defaults to a single layer with relu activation.
--graphSAGE-aggregator mean Specifies the aggreagtion rule used in GraphSAGE. Defaults to mean pooling.
--graphSAGE-nbhd-sizes [10, 5] Specify multiple neighbourhood sizes for sampling in GraphSAGE. Defaults to [10, 5].
--tensorboard If toggles, saves Tensorboard logs for debugging purposes.
--visualize-embeds None If specified with a directory, saves an image of a TSNE 2D projection of the learned embeddings at the specified directory.
--save-spectrum None If specifies, saves the spectrum of the learned embeddings output by the algorithm.

train_embed_link.py arguments

short long default help
-h --help show this help message and exit
--dataset Cora Dataset to perform training on. Available options: Cora,CiteSeer,PubMedDiabetes
--emb-size 128 Embedding dimension. Defaults to 128.
--reg-weight 0.0 Weight to use for L2 regularization. If norm_reg is True, then reg_weight/num_of_nodes is used instead.
--norm-reg Boolean for whether to normalize the L2 regularization weight by the number of nodes in the graph. Defaults to false.
--method node2vec Algorithm to perform training on. Available options: node2vec,GraphSAGE,GCN,DGI
--verbose 1 Level of verbosity. Defaults to 1.
--epochs 5 Number of epochs through the dataset to be used for training.
--optimizer Adam Optimization algorithm to use for training.
--learning-rate 0.001 Learning rate to use for optimization.
--batch-size 64 Batch size used for training.
--test-split 0.1 Split of edge/non-edge set to be used for testing.
--output-fname None If not None, saves the hyperparameters and testing results to a .json file with filename given by the argument.
--node2vec-p 1.0 Hyperparameter governing probability of returning to source node.
--node2vec-q 1.0 Hyperparameter governing probability of moving to a node away from the source node.
--node2vec-walk-number 50 Number of walks used to generate a sample for node2vec.
--node2vec-walk-length 5 Walk length to use for node2vec.
--gcn-activation ['relu'] Specifies layers in terms of their output activation (either relu or linear), with the number of arguments determining the length of the GCN. Defaults to a single layer with relu activation.
--graphSAGE-aggregator mean Specifies the aggreagtion rule used in GraphSAGE. Defaults to mean pooling.
--graphSAGE-nbhd-sizes [10, 5] Specify multiple neighbourhood sizes for sampling in GraphSAGE. Defaults to [25, 10].
Owner
Andrew Davison
Andrew Davison
GitHub Repository
High level network definitions with pre-trained weights in TensorFlow

TensorNets High level network definitions with pre-trained weights in TensorFlow (tested with 2.1.0 = TF = 1.4.0). Guiding principles Applicability.

Taehoon Lee 1k Dec 13, 2022
An implementation of RetinaNet in PyTorch.

RetinaNet An implementation of RetinaNet in PyTorch. Installation Training COCO 2017 Pascal VOC Custom Dataset Evaluation Todo Credits Installation In

Conner Vercellino 297 Jan 04, 2023
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning. CVPR 2018

Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning Tensorflow code and models for the paper: Large Scale Fine-Grained Categ

Yin Cui 187 Oct 01, 2022
Motion planning environment for Sampling-based Planners

Sampling-Based Motion Planners' Testing Environment Sampling-based motion planners' testing environment (sbp-env) is a full feature framework to quick

Soraxas 23 Aug 23, 2022
Implementation for our ICCV 2021 paper: Dual-Camera Super-Resolution with Aligned Attention Modules

DCSR: Dual Camera Super-Resolution Implementation for our ICCV 2021 oral paper: Dual-Camera Super-Resolution with Aligned Attention Modules paper | pr

Tengfei Wang 110 Dec 20, 2022
A texturizer that I just made. Nothing special here.

texturizer This is a little project that I did with an hour's time. It texturizes an image given a image and a texture to texturize it with. There is

1 Nov 11, 2021
Deep motion transfer

animation-with-keypoint-mask Paper The right most square is the final result. Softmax mask (circles): \ Heatmap mask: \ conda env create -f environmen

9 Nov 01, 2022
Supplementary code for the AISTATS 2021 paper "Matern Gaussian Processes on Graphs".

Matern Gaussian Processes on Graphs This repo provides an extension for gpflow with Matérn kernels, inducing variables and trainable models implemente

41 Dec 17, 2022
Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.

Translated in 🇰🇷 Korean/ Ludwig is a toolbox that allows users to train and test deep learning models without the need to write code. It is built on

<

Ludwig 8.7k Jan 05, 2023
Official PaddlePaddle implementation of Paint Transformer

Paint Transformer: Feed Forward Neural Painting with Stroke Prediction [Paper] [Paddle Implementation] Update We have optimized the serial inference p

TianweiLin 284 Dec 31, 2022
Accelerated deep learning R&D

Accelerated deep learning R&D PyTorch framework for Deep Learning research and development. It focuses on reproducibility, rapid experimentation, and

Catalyst-Team 3.1k Jan 06, 2023
Official repository for the ICLR 2021 paper Evaluating the Disentanglement of Deep Generative Models with Manifold Topology

Official repository for the ICLR 2021 paper Evaluating the Disentanglement of Deep Generative Models with Manifold Topology Sharon Zhou, Eric Zelikman

Stanford Machine Learning Group 34 Nov 16, 2022
Facestar dataset. High quality audio-visual recordings of human conversational speech.

Facestar Dataset Description Existing audio-visual datasets for human speech are either captured in a clean, controlled environment but contain only a

Meta Research 87 Dec 21, 2022
RoFormer_pytorch

PyTorch RoFormer 原版Tensorflow权重(https://github.com/ZhuiyiTechnology/roformer) chinese_roformer_L-12_H-768_A-12.zip (提取码：xy9x) 已经转化为PyTorch权重 chinese_r

yujun 283 Dec 12, 2022
TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction

TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction TSDF++ is a novel multi-object TSDF formulation that can encode mult

ETHZ ASL 130 Dec 29, 2022
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

Benedek Rozemberczki 201 Nov 09, 2022
Chinese Mandarin tts text-to-speech 中文 (普通话) 语音 合成 , by fastspeech 2 , implemented in pytorch, using waveglow as vocoder,

Chinese mandarin text to speech based on Fastspeech2 and Unet This is a modification and adpation of fastspeech2 to mandrin(普通话）. Many modifications t

291 Jan 02, 2023
QR2Pass-project - A proof of concept for an alternative (passwordless) authentication system to a web server

QR2Pass This is a proof of concept for an alternative (passwordless) authenticat

4 Dec 09, 2022
code for Multi-scale Matching Networks for Semantic Correspondence, ICCV

MMNet This repo is the official implementation of ICCV 2021 paper "Multi-scale Matching Networks for Semantic Correspondence.". Pre-requisite conda cr

joey zhao 25 Dec 12, 2022
Winners of DrivenData's Overhead Geopose Challenge

Winners of DrivenData's Overhead Geopose Challenge

DrivenData 22 Aug 04, 2022