Awesome Explainable Graph Reasoning
A collection of research papers and software related to explainability in graph machine learning.
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A collection of research papers and software related to explainability in graph machine learning.
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Add new citation: Numeroso et al.
opened by danilonumeroso 1Hi 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
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added GCExplainer
opened by sbonner0 1You 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.
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Added new references
opened by nomisto 0Two 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)
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Include one more paper from NeurIPS 2020
opened by joaquincabezas 0The 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.
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Overwhelming amount of papers
opened by joaquincabezas 1Hi, 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
Releases(v_00001)
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v_00001(Dec 2, 2021)
Auralisation of learned features in CNN (for audio)
AuralisationCNN This repo is for an example of auralisastion of CNNs that is demonstrated on ISMIR 2015. Files auralise.py: includes all required func
39 Nov 19, 2022
Neural network visualization toolkit for tf.keras
Neural network visualization toolkit for tf.keras
262 Dec 19, 2022
Algorithms for monitoring and explaining machine learning models
Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-qual
1.9k Dec 30, 2022
⬛ Python Individual Conditional Expectation Plot Toolbox
⬛ PyCEbox Python Individual Conditional Expectation Plot Toolbox A Python implementation of individual conditional expecation plots inspired by R's IC
140 Dec 30, 2022
Python Library for Model Interpretation/Explanations
Skater Skater is a unified framework to enable Model Interpretation for all forms of model to help one build an Interpretable machine learning system
1k Dec 27, 2022
An intuitive library to add plotting functionality to scikit-learn objects.
Welcome to Scikit-plot Single line functions for detailed visualizations The quickest and easiest way to go from analysis... ...to this. Scikit-plot i
2.3k Dec 31, 2022
Code for "High-Precision Model-Agnostic Explanations" paper
Anchor This repository has code for the paper High-Precision Model-Agnostic Explanations. An anchor explanation is a rule that sufficiently “anchors”
735 Jan 05, 2023
Model analysis tools for TensorFlow
TensorFlow Model Analysis TensorFlow Model Analysis (TFMA) is a library for evaluating TensorFlow models. It allows users to evaluate their models on
1.2k Dec 26, 2022
FairML - is a python toolbox auditing the machine learning models for bias.
======== FairML: Auditing Black-Box Predictive Models FairML is a python toolbox auditing the machine learning models for bias. Description Predictive
338 Nov 09, 2022
Lime: Explaining the predictions of any machine learning classifier
lime This project is about explaining what machine learning classifiers (or models) are doing. At the moment, we support explaining individual predict
10.3k Jan 01, 2023
Delve is a Python package for analyzing the inference dynamics of your PyTorch model.
Delve is a Python package for analyzing the inference dynamics of your PyTorch model.
73 Dec 12, 2022
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
GNNLens2 is an interactive visualization tool for graph neural networks (GNN).
GNNLens2 is an interactive visualization tool for graph neural networks (GNN).
143 Jan 07, 2023
A data-driven approach to quantify the value of classifiers in a machine learning ensemble.
Documentation | External Resources | Research Paper Shapley is a Python library for evaluating binary classifiers in a machine learning ensemble. The
187 Dec 27, 2022
Visualization Toolbox for Long Short Term Memory networks (LSTMs)
Visualization Toolbox for Long Short Term Memory networks (LSTMs)
1.1k Jan 04, 2023
Visual analysis and diagnostic tools to facilitate machine learning model selection.
Yellowbrick Visual analysis and diagnostic tools to facilitate machine learning model selection. What is Yellowbrick? Yellowbrick is a suite of visual
3.9k Dec 30, 2022
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
Convolutional neural network visualization techniques implemented in PyTorch.
This repository contains a number of convolutional neural network visualization techniques implemented in PyTorch.
1 Nov 06, 2021
Python implementation of R package breakDown
pyBreakDown Python implementation of breakDown package (https://github.com/pbiecek/breakDown). Docs: https://pybreakdown.readthedocs.io. Requirements
41 Mar 17, 2022
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
1.2k Dec 29, 2022