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

Last update: Jan 08, 2023

Related tags

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.

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

Related tags

Overview

isvd

To run link prediction on Stanford SNAP and node2vec datasets:

To run semi-supervised node classification on Planetoid datasets

To run link prediction on Stanford OGB DDI

To run link prediction on Stanford OGB ArXiv

Owner

Sami Abu-El-Haija

ISTR: End-to-End Instance Segmentation with Transformers (https://arxiv.org/abs/2105.00637)

Adversarial Adaptation with Distillation for BERT Unsupervised Domain Adaptation

Compositional and Parameter-Efficient Representations for Large Knowledge Graphs

Source code for "Progressive Transformers for End-to-End Sign Language Production" (ECCV 2020)

PyTorch implementation of "Supervised Contrastive Learning" (and SimCLR incidentally)

DECA: Detailed Expression Capture and Animation (SIGGRAPH 2021)

李云龙二次元风格化!打滚卖萌，使用了animeGANv2进行了视频的风格迁移

RaceBERT -- A transformer based model to predict race and ethnicty from names

Kaggle Ultrasound Nerve Segmentation competition [Keras]

Tooling for the Common Objects In 3D dataset.

Callable PyTrees and filtered JIT/grad transformations => neural networks in JAX.

Face recognition. Redefined.

IOT: Instance-wise Layer Reordering for Transformer Structures

Unofficial Implementation of Oboe (SIGCOMM'18').

Graph-total-spanning-trees - A Python script to get total number of Spanning Trees in a Graph

Custom IMDB Dataset is extracted between 2020-2021 and custom distilBERT model is trained for movie success probability prediction

Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

Official project repository for 'Normality-Calibrated Autoencoder for Unsupervised Anomaly Detection on Data Contamination'

Segmentation Training Pipeline

Implementation of paper "Towards a Unified View of Parameter-Efficient Transfer Learning"