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

MAUS: A Dataset for Mental Workload Assessment Using Wearable Sensor - Baseline system

An air quality monitoring service with a Raspberry Pi and a SDS011 sensor.

Simple and Distributed Machine Learning

Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation (CVPR 2021)

ImageNet Adversarial Image Evaluation

2020 CCF大数据与计算智能大赛-非结构化商业文本信息中隐私信息识别-第7名方案

Code for "MetaMorph: Learning Universal Controllers with Transformers", Gupta et al, ICLR 2022

Pytorch port of Google Research's LEAF Audio paper

Public implementation of the Convolutional Motif Kernel Network (CMKN) architecture

An introduction to bioimage analysis - http://bioimagebook.github.io

The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization

Weak-supervised Visual Geo-localization via Attention-based Knowledge Distillation

Reference implementation of code generation projects from Facebook AI Research. General toolkit to apply machine learning to code, from dataset creation to model training and evaluation. Comes with pretrained models.

ELSED: Enhanced Line SEgment Drawing

TensorRT examples (Jetson, Python/C++)(object detection)

Material for my PyConDE & PyData Berlin 2022 Talk "5 Steps to Speed Up Your Data-Analysis on a Single Core"

[CIKM 2019] Code and dataset for "Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Prediction"

Code for "Universal inference meets random projections: a scalable test for log-concavity"

A pytorch-version implementation codes of paper: "BSN++: Complementary Boundary Regressor with Scale-Balanced Relation Modeling for Temporal Action Proposal Generation"

Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite and .pb from .tflite.