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
Implementation of the Point Transformer layer, in Pytorch

Point Transformer - Pytorch Implementation of the Point Transformer self-attention layer, in Pytorch. The simple circuit above seemed to have allowed

Phil Wang 501 Jan 03, 2023
Source code for The Power of Many: A Physarum Swarm Steiner Tree Algorithm

Physarum-Swarm-Steiner-Algo Source code for The Power of Many: A Physarum Steiner Tree Algorithm Code implements ideas from the following papers: Sher

Sheryl Hsu 2 Mar 28, 2022
This repository contains various models targetting multimodal representation learning, multimodal fusion for downstream tasks such as multimodal sentiment analysis.

Multimodal Deep Learning 🎆 🎆 🎆 Announcing the multimodal deep learning repository that contains implementation of various deep learning-based model

Deep Cognition and Language Research (DeCLaRe) Lab 398 Dec 30, 2022
DrWhy is the collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.

Responsible Machine Learning With Great Power Comes Great Responsibility. Voltaire (well, maybe) How to develop machine learning models in a responsib

Model Oriented 590 Dec 26, 2022
BADet: Boundary-Aware 3D Object Detection from Point Clouds (Pattern Recognition 2022)

BADet: Boundary-Aware 3D Object Detection from Point Clouds (Pattern Recognition

Rui Qian 17 Dec 12, 2022
PyTorch implementation of paper "IBRNet: Learning Multi-View Image-Based Rendering", CVPR 2021.

IBRNet: Learning Multi-View Image-Based Rendering PyTorch implementation of paper "IBRNet: Learning Multi-View Image-Based Rendering", CVPR 2021. IBRN

Google Interns 371 Jan 03, 2023
Face and Pose detector that emits MQTT events when a face or human body is detected and not detected.

Face Detect MQTT Face or Pose detector that emits MQTT events when a face or human body is detected and not detected. I built this as an alternative t

Jacob Morris 38 Oct 21, 2022
Developed an optimized algorithm which finds the most optimal path between 2 points in a 3D Maze using various AI search techniques like BFS, DFS, UCS, Greedy BFS and A*

Developed an optimized algorithm which finds the most optimal path between 2 points in a 3D Maze using various AI search techniques like BFS, DFS, UCS, Greedy BFS and A*. The algorithm was extremely

1 Mar 28, 2022
optimization routines for hyperparameter tuning

Hyperopt: Distributed Hyperparameter Optimization Hyperopt is a Python library for serial and parallel optimization over awkward search spaces, which

Marc Claesen 398 Nov 09, 2022
Machine learning Bot detection technique, based on United States election dataset

Machine learning Bot detection technique, based on United States election dataset (2020). Current github repo provides implementation described in pap

Alexander Shevtsov 4 Nov 20, 2022
A Context-aware Visual Attention-based training pipeline for Object Detection from a Webpage screenshot!

CoVA: Context-aware Visual Attention for Webpage Information Extraction Abstract Webpage information extraction (WIE) is an important step to create k

Keval Morabia 41 Jan 01, 2023
Official Code for "Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning"

CMSF Official Code for "Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning" Requirements Python = 3.7.6 PyTorch

4 Nov 25, 2022
An All-MLP solution for Vision, from Google AI

MLP Mixer - Pytorch An All-MLP solution for Vision, from Google AI, in Pytorch. No convolutions nor attention needed! Yannic Kilcher video Install $ p

Phil Wang 784 Jan 06, 2023
Code for Generating Disentangled Arguments with Prompts: A Simple Event Extraction Framework that Works

GDAP Code for Generating Disentangled Arguments with Prompts: A Simple Event Extraction Framework that Works Environment Python (verified: v3.8) CUDA

45 Oct 29, 2022
These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations"

Few-shot-NLEs These are the materials for the paper "Few-Shot Out-of-Domain Transfer Learning of Natural Language Explanations". You can find the smal

Yordan Yordanov 0 Oct 21, 2022
Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering

Consensus Learning from Heterogeneous Objectives for One-Class Collaborative Filtering This repository provides the source code of "Consensus Learning

SeongKu-Kang 6 Apr 29, 2022
A Python Library for Graph Outlier Detection (Anomaly Detection)

PyGOD is a Python library for graph outlier detection (anomaly detection). This exciting yet challenging field has many key applications, e.g., detect

PyGOD Team 757 Jan 04, 2023
C3d-pytorch - Pytorch porting of C3D network, with Sports1M weights

C3D for pytorch This is a pytorch porting of the network presented in the paper Learning Spatiotemporal Features with 3D Convolutional Networks How to

Davide Abati 311 Jan 06, 2023
PyTorch code for the paper: FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning

FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning This is the PyTorch implementation of our paper: FeatMatch: Feature-Based Augmentat

43 Nov 19, 2022
Exposure Time Calculator (ETC) and radial velocity precision estimator for the Near InfraRed Planet Searcher (NIRPS) spectrograph

NIRPS-ETC Exposure Time Calculator (ETC) and radial velocity precision estimator for the Near InfraRed Planet Searcher (NIRPS) spectrograph February 2

Nolan Grieves 2 Sep 15, 2022