Source code for From Stars to Subgraphs

Related tags

Deep LearningGNNAsKernel
Overview

GNNAsKernel

Official code for From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness

Visualizations

GNN-AK(+)

GNN-AK

GNN-AK(+) with SubgraphDrop

GNN-AK-S: GNN-AK with SubgraphDrop

Setup

# params
# 10/6/2021, newest packages. 
ENV=gnn_ak
CUDA=11.1
TORCH=1.9.1
PYG=2.0.1

# create env 
conda create --name $ENV python=3.9 -y
conda activate $ENV

# install pytorch 
conda install pytorch=$TORCH torchvision torchaudio cudatoolkit=$cuda -c pytorch -c nvidia -y

# install pyg2.0
conda install pyg=$PYG -c pyg -c conda-forge -y

# install ogb 
pip install ogb

# install rdkit
conda install -c conda-forge rdkit -y

# update yacs and tensorboard
pip install yacs==0.1.8 --force  # PyG currently use 0.1.6 which doesn't support None argument. 
pip install tensorboard
pip install matplotlib

Code structure

core/ contains all source code.
train/ contains all scripts for available datasets.

  • Subgraph extraction is implemented as data transform operator in PyG. See core/transform.py. The transform layer will built the mapping from original nodes and edges to all subgraphs.
  • The mappings are used directly in GNN-AK(+) to online build the combined subgraphs for each graph, see core/model.py. (For each graph with N node, N subgraphs are combined to a gaint subgraph first. Then for batch, all combined gaint subgraphs are combined again.)
  • SubgraphDrop is implemented inside core/transform.py, see here. And the usage in core/model.py.
  • core/model_utils/pyg_gnn_wrapper.py is the place to add your self-designed GNN layer X and then use X-AK(+) on fly~

Hyperparameters

See core/config.py for all options.

Run normal GNNs

See core/model_utls/pyg_gnn_wrapper.py for more options.

Custom new GNN convolutional layer 'X' can be plugged in core/model_utls/pyg_gnn_wrapper.py, and use 'X' as model.gnn_type option.

# Run different normal GNNs 
python -m train.zinc model.mini_layers 0 model.gnn_type GINEConv
python -m train.zinc model.mini_layers 0 model.gnn_type SimplifiedPNAConv
python -m train.zinc model.mini_layers 0 model.gnn_type GCNConv
python -m train.zinc model.mini_layers 0 model.gnn_type GATConv
python -m train.zinc model.mini_layers 0 model.gnn_type ...

python -m train.zinc model.num_layers 6 model.mini_layers 0 model.gnn_type GCNConv # 6-layer GCN

Run different datasets

See all available datasets under train folder.

# Run different datasets
python -m train.zinc 
python -m train.cifar10 
python -m train.counting 
python -m train.graph_property 
python -m ...

Run GNN-AK

Fully theoretically explained by Subgraph-1-WL*.

Use: model.mini_layers 1 (or >1) model.embs "(0,1)" model.hops_dim 0 

python -m train.zinc model.mini_layers 1 model.gnn_type GINEConv model.embs "(0,1)" model.hops_dim 0

Run GNN-AK+

At least as powerful as GNN-AK (or more powerful).

Use: model.mini_layers 1 (or >1) model.embs "(0,1,2)" model.hops_dim 16
These are set as default. See core/config.py.

# Run GNN-AK+ with different normal GNNs
python -m train.zinc model.mini_layers 1 model.gnn_type GINEConv            # 1-layer base model
python -m train.zinc model.mini_layers 1 model.gnn_type SimplifiedPNAConv   # 1-layer base model
python -m train.zinc model.mini_layers 2 model.gnn_type GINEConv            # 2-layer base model
python -m train.zinc model.mini_layers 2 model.gnn_type SimplifiedPNAConv   # 2-layer base model

Run with different number of GNN-AK(+) iterations

Changing number of outer layers.

python -m train.zinc model.num_layers 4 
python -m train.zinc model.num_layers 6 
python -m train.zinc model.num_layers 8

Run with different subgraph patterns

See core/transform.py for detailed implementation.

python -m train.zinc subgraph.hops 2      # 2-hop egonet
python -m train.zinc subgraph.hops 3      # 3-hop egonet

# Run with random-walk subgraphs based on node2vec 
python -m train.zinc subgraph.hops 0 subgraph.walk_length 10 subgraph.walk_p 1.0 subgraph.walk_q 1.0

Run GNN-AK(+) with SubgraphDrop

See option sampling section under core/config.py.

Change sampling.redundancy(R in the paper) to change the resource usage.

python -m train.zinc sampling.mode shortest_path sampling.redundancy 1 sampling.stride 5 sampling.batch_factor 4
python -m train.zinc sampling.mode shortest_path sampling.redundancy 3 sampling.stride 5 sampling.batch_factor 4
python -m train.zinc sampling.mode shortest_path sampling.redundancy 5 sampling.stride 5 sampling.batch_factor 4


python -m train.cifar10 sampling.mode random sampling.redundancy 1 sampling.random_rate 0.07 sampling.batch_factor 8 
python -m train.cifar10 sampling.mode random sampling.redundancy 3 sampling.random_rate 0.21 sampling.batch_factor 8 
python -m train.cifar10 sampling.mode random sampling.redundancy 5 sampling.random_rate 0.35 sampling.batch_factor 8 
## Note: sampling.random_rate = 0.07*sampling.redundancy. 0.07 is set based on dataset.

Results

GNN-AK boosts expressiveness

GNN-AK boosts expressiveness

GNN-AK boosts practical performance

GNN-AK boosts practical performance

Cite

Please cite our work if you use our code!

@inproceedings{
anonymous2022from,
title={From Stars to Subgraphs: Uplifting Any {GNN} with Local Structure Awareness},
author={Anonymous},
booktitle={Submitted to The Tenth International Conference on Learning Representations },
year={2022},
url={https://openreview.net/forum?id=Mspk_WYKoEH},
note={under review}
}
Owner
GitHub Repository
PINN(s): Physics-Informed Neural Network(s) for von Karman vortex street

PINN(s): Physics-Informed Neural Network(s) for von Karman vortex street This is

ShotaDEGUCHI 2 Apr 18, 2022
Deep Learning agent of Starcraft2, similar to AlphaStar of DeepMind except size of network.

Introduction This repository is for Deep Learning agent of Starcraft2. It is very similar to AlphaStar of DeepMind except size of network. I only test

Dohyeong Kim 136 Jan 04, 2023
This repository contains the code and models for the following paper.

DC-ShadowNet Introduction This is an implementation of the following paper DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised

AuAgCu 65 Dec 27, 2022
Split Variational AutoEncoder

Split-VAE Split Variational AutoEncoder Introduction This repository contains and implemementation of a Split Variational AutoEncoder (SVAE). In a SVA

Andrea Asperti 2 Sep 02, 2022
Human-Pose-and-Motion History

Human Pose and Motion Scientist Approach Eadweard Muybridge, The Galloping Horse Portfolio, 1887 Etienne-Jules Marey, Descent of Inclined Plane, Chron

Daito Manabe 47 Dec 16, 2022
Code for the Weighted, Accelerated and Restarted Primal-dual algorithm. This algorithm achieves stable linear convergence for reconstruction from undersampled noisy measurements under an approximate sharpness condition. See the paper for details.

WARPd Code for the Weighted, Accelerated and Restarted Primal-dual algorithm. This algorithm achieves stable linear convergence for reconstruction fro

Matthew Colbrook 1 Apr 08, 2022
The versatile ocean simulator, in pure Python, powered by JAX.

Veros is the versatile ocean simulator -- it aims to be a powerful tool that makes high-performance ocean modeling approachable and fun. Because Veros

TeamOcean 245 Dec 20, 2022
No Code AI/ML platform

NoCodeAIML No Code AI/ML platform - Community Edition Video credits: Uday Kiran Typical No Code AI/ML Platform will have features like drag and drop,

Bhagvan Kommadi 5 Jan 28, 2022
Head2Toe: Utilizing Intermediate Representations for Better OOD Generalization

Head2Toe: Utilizing Intermediate Representations for Better OOD Generalization Code for reproducing our results in the Head2Toe paper. Paper: arxiv.or

Google Research 62 Dec 12, 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
This is an official implementation of the paper "Distance-aware Quantization", accepted to ICCV2021.

PyTorch implementation of DAQ This is an official implementation of the paper "Distance-aware Quantization", accepted to ICCV2021. For more informatio

CV Lab @ Yonsei University 36 Nov 04, 2022
Unofficial Pytorch Implementation of WaveGrad2

WaveGrad 2 — Unofficial PyTorch Implementation WaveGrad 2: Iterative Refinement for Text-to-Speech Synthesis Unofficial PyTorch+Lightning Implementati

MINDs Lab 104 Nov 29, 2022
Notebooks em Python para Métodos Eletromagnéticos

GeoSci Labs This is a repository of code used to power the notebooks and interactive examples for https://em.geosci.xyz and https://gpg.geosci.xyz. Th

Victor Cezar Tocantins 1 Nov 16, 2021
Keras implementation of PersonLab for Multi-Person Pose Estimation and Instance Segmentation.

PersonLab This is a Keras implementation of PersonLab for Multi-Person Pose Estimation and Instance Segmentation. The model predicts heatmaps and vari

OCTI 160 Dec 21, 2022
Code accompanying paper: Meta-Learning to Improve Pre-Training

Meta-Learning to Improve Pre-Training This folder contains code to run experiments in the paper Meta-Learning to Improve Pre-Training, NeurIPS 2021. P

28 Dec 31, 2022
This repository provides a basic implementation of our GCPR 2021 paper "Learning Conditional Invariance through Cycle Consistency"

Learning Conditional Invariance through Cycle Consistency This repository provides a basic TensorFlow 1 implementation of the proposed model in our GC

BMDA - University of Basel 1 Nov 04, 2022
Source code for the BMVC-2021 paper "SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation".

SimReg: A Simple Regression Based Framework for Self-supervised Knowledge Distillation Source code for the paper "SimReg: Regression as a Simple Yet E

9 Oct 15, 2022
Noise Conditional Score Networks (NeurIPS 2019, Oral)

Generative Modeling by Estimating Gradients of the Data Distribution This repo contains the official implementation for the NeurIPS 2019 paper Generat

451 Dec 26, 2022
Official PyTorch Implementation of Unsupervised Learning of Scene Flow Estimation Fusing with Local Rigidity

UnRigidFlow This is the official PyTorch implementation of UnRigidFlow (IJCAI2019). Here are two sample results (~10MB gif for each) of our unsupervis

Liang Liu 28 Nov 16, 2022
City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces

City Surfaces: City-scale Semantic Segmentation of Sidewalk Surfaces Paper Temporary GitHub page for City Surfaces paper. More soon! While designing s

14 Nov 10, 2022