Code for 2021 NeurIPS --- Towards Multi-Grained Explainability for Graph Neural Networks

Related tags

Deep LearningReFine
Overview

ReFine: Multi-Grained Explainability for GNNs

We are trying hard to update the code, but it may take a while to complete due to our tight schedule recently. Thank you for your waiting!

Installation

Requirements

  • CPU or NVIDIA GPU, Linux, Python 3.7
  • PyTorch, various Python packages

Main Packages

  1. Pytorch Geometric. Official Download.
# We use TORCH version 1.6.0
CUDA=cu101
TORCH=1.6.0 
pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-geometric
  1. Visual Genome. Google Drive Download. This is used for preprocessing the VG-5 dataset and visualizing the generated explanations. Manually download it to the same directory as data/. (Yes, this package can be installed using pip or API, but we find it slow to use).

Datasets

  1. The processed raw data for BA-3motif is available in the data/ folder.
  2. Datasets MNIST, Mutagenicity will be automatically downloaded when training models.
  3. We select and label 4444 graphs from https://visualgenome.org/ to construct the VG-5 dataset. The graphs are labeled with five classes: stadium, street, farm, surfing, forest. Each graph contains regions of the objects as the nodes, while edges indicate the relationships between object nodes.

Download the dataset from Google Drive. Arrange the dir as

data ---BA3
 |------VG
        |---raw

Please remember to cite Visual Genome (bibtex) if you use our VG-5 dataset.

Training GNNs

cd gnns/
python ba3motif_gnn.py --epoch 100 --num_unit 2 --batch_size 128

The trained GNNs will be saved in param/gnns.

Explaining the Predictions

code is coming soon

Evaluation & Visualization

code is coming soon

Citation

Please cite our paper if you find the repository useful.

@inproceedings{2021refine,
  title={Towards Multi-Grained Explainability for Graph Neural Networks },
  author={Wang, Xiang and Wu, Ying-Xin and Zhang, An and He, Xiangnan and Chua, Tat-Seng},
  booktitle={Proceedings of the 35th Conference on Neural Information Processing Systems},
  year={2021} 
}
Owner
Shirley (Ying-Xin) Wu
Senior Undergraduate @ LDS, School of Data Science. [email protected]
Shirley (Ying-Xin) Wu
[ICML'21] Estimate the accuracy of the classifier in various environments through self-supervision

What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments? [Paper] [ICML'21 Project] PyTorch Implementation T

24 Oct 26, 2022
A package, and script, to perform imaging transcriptomics on a neuroimaging scan.

Imaging Transcriptomics Imaging transcriptomics is a methodology that allows to identify patterns of correlation between gene expression and some prop

Alessio Giacomel 10 Dec 27, 2022
An efficient 3D semantic segmentation framework for Urban-scale point clouds like SensatUrban, Campus3D, etc.

An efficient 3D semantic segmentation framework for Urban-scale point clouds like SensatUrban, Campus3D, etc.

Zou 33 Jan 03, 2023
ByteTrack with ReID module following the paradigm of FairMOT, tracking strategy is borrowed from FairMOT/JDE.

ByteTrack_ReID ByteTrack is the SOTA tracker in MOT benchmarks with strong detector YOLOX and a simple association strategy only based on motion infor

Han GuangXin 46 Dec 29, 2022
vit for few-shot classification

Few-Shot ViT Requirements PyTorch (= 1.9) TorchVision timm (latest) einops tqdm numpy scikit-learn scipy argparse tensorboardx Pretrained Checkpoints

Martin Dong 26 Nov 30, 2022
Code & Models for Temporal Segment Networks (TSN) in ECCV 2016

Temporal Segment Networks (TSN) We have released MMAction, a full-fledged action understanding toolbox based on PyTorch. It includes implementation fo

1.4k Jan 01, 2023
Official implementation of "Open-set Label Noise Can Improve Robustness Against Inherent Label Noise" (NeurIPS 2021)

Open-set Label Noise Can Improve Robustness Against Inherent Label Noise NeurIPS 2021: This repository is the official implementation of ODNL. Require

Hongxin Wei 12 Dec 07, 2022
Monitora la qualità della ricezione dei segnali radio nelle province siciliane.

FMap-server Monitora la qualità della ricezione dei segnali radio nelle province siciliane. Conversion data Frequency - StationName maps are stored in

Triglie 5 May 24, 2021
Metric learning algorithms in Python

metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met

1.3k Jan 02, 2023
'A C2C E-COMMERCE TRUST MODEL BASED ON REPUTATION' Python implementation

Project description A library providing functionalities to calculate reputation and degree of trust on C2C ecommerce platforms. The work is fully base

Davide Bigotti 2 Dec 14, 2022
Progressive Growing of GANs for Improved Quality, Stability, and Variation

Progressive Growing of GANs for Improved Quality, Stability, and Variation — Official TensorFlow implementation of the ICLR 2018 paper Tero Karras (NV

Tero Karras 5.9k Jan 05, 2023
Voice of Pajlada with model and weights.

Pajlada TTS Stripped down version of ForwardTacotron (https://github.com/as-ideas/ForwardTacotron) with pretrained weights for Pajlada's (https://gith

6 Sep 03, 2021
BaseCls BaseCls 是一个基于 MegEngine 的预训练模型库,帮助大家挑选或训练出更适合自己科研或者业务的模型结构

BaseCls BaseCls 是一个基于 MegEngine 的预训练模型库,帮助大家挑选或训练出更适合自己科研或者业务的模型结构。 文档地址:https://basecls.readthedocs.io 安装 安装环境 BaseCls 需要 Python = 3.6。 BaseCls 依赖 M

MEGVII Research 28 Dec 23, 2022
A solution to ensure Crowd Management with Contactless and Safe systems.

CovidTrack A Solution to ensure Crowd Management with Contactless and Safe systems. ML Model Mask Detection Social Distancing Detection Analytics Page

Om Khare 1 Nov 10, 2021
Code for "Steerable Pyramid Transform Enables Robust Left Ventricle Quantification"

Code for "Steerable Pyramid Transform Enables Robust Left Ventricle Quantification" This is an end-to-end framework for accurate and robust left ventr

2 Jul 09, 2022
This is a pytorch implementation of the NeurIPS paper GAN Memory with No Forgetting.

GAN Memory for Lifelong learning This is a pytorch implementation of the NeurIPS paper GAN Memory with No Forgetting. Please consider citing our paper

Miaoyun Zhao 43 Dec 27, 2022
This repository contains source code for the Situated Interactive Language Grounding (SILG) benchmark

SILG This repository contains source code for the Situated Interactive Language Grounding (SILG) benchmark. If you find this work helpful, please cons

Victor Zhong 17 Nov 27, 2022
Genetic Programming in Python, with a scikit-learn inspired API

Welcome to gplearn! gplearn implements Genetic Programming in Python, with a scikit-learn inspired and compatible API. While Genetic Programming (GP)

Trevor Stephens 1.3k Jan 03, 2023
natural image generation using ConvNets

The Eyescream Project Generating Natural Images using Neural Networks. For our research summary on this work, please read the Arxiv paper: http://arxi

Meta Archive 601 Nov 23, 2022
Repository for "Improving evidential deep learning via multi-task learning," published in AAAI2022

Improving evidential deep learning via multi task learning It is a repository of AAAI2022 paper, “Improving evidential deep learning via multi-task le

deargen 11 Nov 19, 2022