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
Project ArXiv Citation Network

Project ArXiv Citation Network Overview This project involved the analysis of the ArXiv citation network. Usage The complete code of this project is i

Dennis Núñez-Fernández 5 Oct 20, 2022
Semi-supervised Transfer Learning for Image Rain Removal. In CVPR 2019.

Semi-supervised Transfer Learning for Image Rain Removal This package contains the Python implementation of "Semi-supervised Transfer Learning for Ima

Wei Wei 59 Dec 26, 2022
My implementation of transformers related papers for computer vision in pytorch

vision_transformers This is my personnal repo to implement new transofrmers based and other computer vision DL models I am currenlty working without a

samsja 1 Nov 10, 2021
Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval (NeurIPS'21)

Baleen Baleen is a state-of-the-art model for multi-hop reasoning, enabling scalable multi-hop search over massive collections for knowledge-intensive

Stanford Future Data Systems 22 Dec 05, 2022
Code for the CVPR2021 paper "Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition"

Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition This repository contains code for the CVPR2021 paper "Patch-NetV

QVPR 368 Jan 06, 2023
XtremeDistil framework for distilling/compressing massive multilingual neural network models to tiny and efficient models for AI at scale

XtremeDistilTransformers for Distilling Massive Multilingual Neural Networks ACL 2020 Microsoft Research [Paper] [Video] Releasing [XtremeDistilTransf

Microsoft 125 Jan 04, 2023
Code repo for "FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation" (ICCV 2021)

FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation (ICCV 2021) This repository contains the implementation of th

Yuhang Zang 21 Dec 17, 2022
FCN (Fully Convolutional Network) is deep fully convolutional neural network architecture for semantic pixel-wise segmentation

FCN_via_Keras FCN FCN (Fully Convolutional Network) is deep fully convolutional neural network architecture for semantic pixel-wise segmentation. This

Kento Watanabe 48 Aug 30, 2022
Dynamic Graph Event Detection

DyGED Dynamic Graph Event Detection Get Started pip install -r requirements.txt TODO Paper link to arxiv, and how to cite. Twitter Weather dataset tra

Mert Koşan 3 May 09, 2022
DRIFT is a tool for Diachronic Analysis of Scientific Literature.

About DRIFT is a tool for Diachronic Analysis of Scientific Literature. The application offers user-friendly and customizable utilities for two modes:

Rajaswa Patil 108 Dec 12, 2022
Functional deep learning

Pipeline abstractions for deep learning. Full documentation here: https://lf1-io.github.io/padl/ PADL: is a pipeline builder for PyTorch. may be used

LF1 101 Nov 09, 2022
Real-time pose estimation accelerated with NVIDIA TensorRT

trt_pose Want to detect hand poses? Check out the new trt_pose_hand project for real-time hand pose and gesture recognition! trt_pose is aimed at enab

NVIDIA AI IOT 803 Jan 06, 2023
Nightmare-Writeup - Writeup for the Nightmare CTF Challenge from 2022 DiceCTF

Nightmare: One Byte to ROP // Alternate Solution TLDR: One byte write, no leak.

1 Feb 17, 2022
Package to compute Mauve, a similarity score between neural text and human text. Install with `pip install mauve-text`.

MAUVE MAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE

Krishna Pillutla 182 Jan 02, 2023
LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models

LaneDet is an open source lane detection toolbox based on PyTorch that aims to pull together a wide variety of state-of-the-art lane detection models. Developers can reproduce these SOTA methods and

TuZheng 405 Jan 04, 2023
Learning to Initialize Neural Networks for Stable and Efficient Training

GradInit This repository hosts the code for experiments in the paper, GradInit: Learning to Initialize Neural Networks for Stable and Efficient Traini

Chen Zhu 124 Dec 30, 2022
The backbone CSPDarkNet of YOLOX.

YOLOX-Backbone The backbone CSPDarkNet of YOLOX. In this project, you can enjoy: CSPDarkNet-S CSPDarkNet-M CSPDarkNet-L CSPDarkNet-X CSPDarkNet-Tiny C

Jianhua Yang 9 Aug 22, 2022
KaziText is a tool for modelling common human errors.

KaziText KaziText is a tool for modelling common human errors. It estimates probabilities of individual error types (so called aspects) from grammatic

ÚFAL 3 Nov 24, 2022
Awesome Graph Classification - A collection of important graph embedding, classification and representation learning papers with implementations.

A collection of graph classification methods, covering embedding, deep learning, graph kernel and factorization papers

Benedek Rozemberczki 4.5k Jan 01, 2023
GAN Image Generator and Characterwise Image Recognizer with python

MODEL SUMMARY 모델의 구조는 크게 6단계로 나뉩니다. STEP 0: Input Image Predict 할 이미지를 모델에 입력합니다. STEP 1: Make Black and White Image STEP 1 은 입력받은 이미지의 글자를 흑색으로, 배경을

Juwan HAN 1 Feb 09, 2022