Official implementation of NeurIPS'21: Implicit SVD for Graph Representation Learning

Related tags

Deep Learningisvd
Overview

isvd

Official implementation of NeurIPS'21: Implicit SVD for Graph Representation Learning

If you find this code useful, you may cite us as:

@inproceedings{haija2021isvd,
  author={Sami Abu-El-Haija AND Hesham Mostafa AND Marcel Nassar AND Valentino Crespi AND Greg Ver Steeg AND Aram Galstyan},
  title={Implicit SVD for Graph Representation Learning},
  booktitle={Advances in Neural Information Processing Systems},
  year={2021},
}

To run link prediction on Stanford SNAP and node2vec datasets:

To embed with rank-32 SVD:

python3 run_snap_linkpred.py --dataset_name=ppi --dim=32
python3 run_snap_linkpred.py --dataset_name=ca-AstroPh --dim=32
python3 run_snap_linkpred.py --dataset_name=ca-HepTh --dim=32
python3 run_snap_linkpred.py --dataset_name=soc-facebook --dim=32

To embed with rank 256 on half of the training edges, determine "best rank" based on the remaining half, then re-run sVD with the best rank on all of training: (note: negative dim causes this logic):

python3 run_snap_linkpred.py --dataset_name=ppi --dim=-256
python3 run_snap_linkpred.py --dataset_name=ca-AstroPh --dim=-256
python3 run_snap_linkpred.py --dataset_name=ca-HepTh --dim=-256
python3 run_snap_linkpred.py --dataset_name=soc-facebook --dim=-256

To run semi-supervised node classification on Planetoid datasets

You must first download the planetoid dataset as:

mkdir -p ~/data
cd ~/data
git clone [email protected]:kimiyoung/planetoid.git

Afterwards, you may navigate back to this directory and run our code as:

python3 run_planetoid.py --dataset=ind.citeseer
python3 run_planetoid.py --dataset=ind.cora
python3 run_planetoid.py --dataset=ind.pubmed

To run link prediction on Stanford OGB DDI

python3 ogb_linkpred_sing_val_net.py

Note the above will download the dataset from Stanford. If you already have it, you may symlink it into directory dataset

To run link prediction on Stanford OGB ArXiv

As our code imports gttf, you must first clone it onto the repo:

git clone [email protected]:isi-usc-edu/gttf.git

Afterwards, you may run as:

python3 final_obgn_mixed_device.py --funetune_device='gpu:0'

Note the above will download the dataset from Stanford. If you already have it, you may symlink it into directory dataset. You may skip the finetune_device argument if you do not have a GPU installed.

Owner
Sami Abu-El-Haija
Sami Abu-El-Haija
GitHub Repository
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

Joe Ortiz 34 Dec 05, 2022
The Noise Contrastive Estimation for softmax output written in Pytorch

An NCE implementation in pytorch About NCE Noise Contrastive Estimation (NCE) is an approximation method that is used to work around the huge computat

Kaiyu Shi 287 Nov 25, 2022
Deploy a ML inference service on a budget in less than 10 lines of code.

BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end.

1.3k Dec 25, 2022
Fuzzy Overclustering (FOC)

Fuzzy Overclustering (FOC) In real-world datasets, we need consistent annotations between annotators to give a certain ground-truth label. However, in

2 Nov 08, 2022
Generating Band-Limited Adversarial Surfaces Using Neural Networks

Generating Band-Limited Adversarial Surfaces Using Neural Networks This is the official repository of the technical report that was published on arXiv

3 Jul 26, 2022
Show Me the Whole World: Towards Entire Item Space Exploration for Interactive Personalized Recommendations

HierarchicyBandit Introduction This is the implementation of WSDM 2022 paper : Show Me the Whole World: Towards Entire Item Space Exploration for Inte

yu song 5 Sep 09, 2022
Zero-shot Learning by Generating Task-specific Adapters

Code for "Zero-shot Learning by Generating Task-specific Adapters" This is the repository containing code for "Zero-shot Learning by Generating Task-s

INK Lab @ USC 11 Dec 17, 2021
Multi-modal Content Creation Model Training Infrastructure including the FACT model (AI Choreographer) implementation.

AI Choreographer: Music Conditioned 3D Dance Generation with AIST++ [ICCV-2021]. Overview This package contains the model implementation and training

Google Research 365 Dec 30, 2022
Manim is an engine for precise programmatic animations, designed for creating explanatory math videos

Manim is an engine for precise programmatic animations, designed for creating explanatory math videos. Note, there are two versions of manim. This rep

Grant Sanderson 49k Jan 09, 2023
Code for paper "Vocabulary Learning via Optimal Transport for Neural Machine Translation"

**Codebase and data are uploaded in progress. ** VOLT(-py) is a vocabulary learning codebase that allows researchers and developers to automaticaly ge

416 Jan 09, 2023
This repository contains code to train and render Mixture of Volumetric Primitives (MVP) models

Mixture of Volumetric Primitives -- Training and Evaluation This repository contains code to train and render Mixture of Volumetric Primitives (MVP) m

Meta Research 125 Dec 29, 2022
Repo for the paper "DiLBERT: Cheap Embeddings for Disease Related Medical NLP"

DiLBERT Repo for the paper "DiLBERT: Cheap Embeddings for Disease Related Medical NLP" Pretrained Model The pretrained model presented in the paper is

Kevin Roitero 2 Dec 15, 2022
Official implementation for "Style Transformer for Image Inversion and Editing" (CVPR 2022)

Style Transformer for Image Inversion and Editing (CVPR2022) https://arxiv.org/abs/2203.07932 Existing GAN inversion methods fail to provide latent co

Xueqi Hu 153 Dec 02, 2022
functorch is a prototype of JAX-like composable function transforms for PyTorch.

functorch is a prototype of JAX-like composable function transforms for PyTorch.

Facebook Research 1.2k Jan 09, 2023
Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models

LMPBT Supplementary code for the Paper entitled ``Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models"

1 Sep 29, 2022
Implementation of the HMAX model of vision in PyTorch

PyTorch implementation of HMAX PyTorch implementation of the HMAX model that closely follows that of the MATLAB implementation of The Laboratory for C

Marijn van Vliet 52 Oct 13, 2022
Network Compression via Central Filter

Network Compression via Central Filter Environments The code has been tested in the following environments: Python 3.8 PyTorch 1.8.1 cuda 10.2 torchsu

2 May 12, 2022
implicit displacement field

Geometry-Consistent Neural Shape Representation with Implicit Displacement Fields [project page][paper][cite] Geometry-Consistent Neural Shape Represe

Yifan Wang 100 Dec 19, 2022
DECA: Detailed Expression Capture and Animation (SIGGRAPH 2021)

DECA: Detailed Expression Capture and Animation (SIGGRAPH2021) input image, aligned reconstruction, animation with various poses & expressions This is

Yao Feng 1.5k Jan 02, 2023
Code base for "On-the-Fly Test-time Adaptation for Medical Image Segmentation"

On-the-Fly Adaptation Official Pytorch Code base for On-the-Fly Test-time Adaptation for Medical Image Segmentation Paper Introduction One major probl

Jeya Maria Jose 17 Nov 10, 2022