A collection of research papers and software related to explainability in graph machine learning.

Last update: Dec 26, 2022

Overview

Awesome Explainable Graph Reasoning

A collection of research papers and software related to explainability in graph machine learning.

License

Apache 2.0

Comments

Add new citation: Numeroso et al.

Hi all, I've added a new reference to a paper of mine related to counterfactual explanations for molecule predictions. I hope this is appreciated :)

Link to paper: https://arxiv.org/abs/2104.08060

opened by danilonumeroso 1
added GCExplainer

You might want to double check this commit is ok - I added a new sub-heading called concept based methods which was not covered by the survey paper the rest of the approaches are categorised into.

opened by sbonner0 1
Added new references
Two papers on rule-based reasoning:

AnyBURL (Meilicke et. al)

SAFRAN (Ott et. al)

And one application note on a web application for visualizing predictions and their explanations using made my the approaches above:

LinkExplorer (Ott et. al)
opened by nomisto 0
Include one more paper from NeurIPS 2020

The work 'Evaluating Attribution for Graph Neural Networks' is particularly useful because of its approach as a benchmarking. It comprises several attribution techniques and GNN architectures.

opened by joaquincabezas 0
Overwhelming amount of papers

Hi, I have been impressed about how fast is this field growing. As I continue reading and learning, I will contribute with papers to make this list even better.

In particular, @flyingdoog is maintaining a list with the papers (grouped by year) at https://github.com/flyingdoog/awesome-graph-explainability-papers that can be interesting to review

opened by joaquincabezas 1

Releases(v_00001)

v_00001(Dec 2, 2021)
This repo covers:

Explainable Predictions

Explainable Reasoning

Software

Theory and Survey Papers

Source code(tar.gz)
Source code(zip)

Owner

AstraZeneca

Data Science & AI: Unlocking new science insights

GitHub Repository

Interactive convnet features visualization for Keras

Quiver Interactive convnet features visualization for Keras The quiver workflow Video Demo Build your model in keras model = Model(...) Launch the vis

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Pytorch Feature Map Extractor

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40 Dec 07, 2022

TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, Korean, Chinese, German and Easy to adapt for other languages)

🤪 TensorFlowTTS provides real-time state-of-the-art speech synthesis architectures such as Tacotron-2, Melgan, Multiband-Melgan, FastSpeech, FastSpeech2 based-on TensorFlow 2. With Tensorflow 2, we c

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treeinterpreter - Interpreting scikit-learn's decision tree and random forest predictions.

TreeInterpreter Package for interpreting scikit-learn's decision tree and random forest predictions. Allows decomposing each prediction into bias and

720 Dec 22, 2022

JittorVis - Visual understanding of deep learning model.

182 Jan 06, 2023

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

Hierarchical neural-net interpretations (ACD) 🧠 Produces hierarchical interpretations for a single prediction made by a pytorch neural network. Offic

111 Jan 03, 2023

Interpretability and explainability of data and machine learning models

AI Explainability 360 (v0.2.1) The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datase

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Bias and Fairness Audit Toolkit

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513 Jan 06, 2023

L2X - Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation.

L2X Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation at ICML 2018,

113 Sep 06, 2022

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

Neural-Backed Decision Trees · Site · Paper · Blog · Video Alvin Wan, *Lisa Dunlap, *Daniel Ho, Jihan Yin, Scott Lee, Henry Jin, Suzanne Petryk, Sarah

556 Dec 20, 2022

Visualizer for neural network, deep learning, and machine learning models

Netron is a viewer for neural network, deep learning and machine learning models. Netron supports ONNX, TensorFlow Lite, Keras, Caffe, Darknet, ncnn,

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ModelChimp is an experiment tracker for Deep Learning and Machine Learning experiments.

ModelChimp What is ModelChimp? ModelChimp is an experiment tracker for Deep Learning and Machine Learning experiments. ModelChimp provides the followi

124 Dec 21, 2022

Quickly and easily create / train a custom DeepDream model

Dream-Creator This project aims to simplify the process of creating a custom DeepDream model by using pretrained GoogleNet models and custom image dat

56 Jan 03, 2023

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

Class Activation Map methods implemented in Pytorch pip install grad-cam ⭐ Comprehensive collection of Pixel Attribution methods for Computer Vision.

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A collection of research papers and software related to explainability in graph machine learning.

Related tags

Overview

Awesome Explainable Graph Reasoning

Contents

Comments

Add new citation: Numeroso et al.

added GCExplainer

Added new references

Include one more paper from NeurIPS 2020

Overwhelming amount of papers

Releases(v_00001)

v_00001(Dec 2, 2021)

Owner

AstraZeneca

Interactive convnet features visualization for Keras

Pytorch Feature Map Extractor

TensorFlowTTS: Real-Time State-of-the-art Speech Synthesis for Tensorflow 2 (supported including English, Korean, Chinese, German and Easy to adapt for other languages)

treeinterpreter - Interpreting scikit-learn's decision tree and random forest predictions.

JittorVis - Visual understanding of deep learning model.

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

Interpretability and explainability of data and machine learning models

Bias and Fairness Audit Toolkit

L2X - Code for replicating the experiments in the paper Learning to Explain: An Information-Theoretic Perspective on Model Interpretation.

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

Visualizer for neural network, deep learning, and machine learning models

ModelChimp is an experiment tracker for Deep Learning and Machine Learning experiments.

Quickly and easily create / train a custom DeepDream model

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

Net2Vis automatically generates abstract visualizations for convolutional neural networks from Keras code.

Code for visualizing the loss landscape of neural nets

A python library for decision tree visualization and model interpretation.

⬛ Python Individual Conditional Expectation Plot Toolbox

Lucid library adapted for PyTorch

Neural network visualization toolkit for tf.keras