Awesome Explainable Graph Reasoning
A collection of research papers and software related to explainability in graph machine learning.
Contents
License
A collection of research papers and software related to explainability in graph machine learning.
Overview
Comments
-
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
-
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.
-
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)
-
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.
-
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)
-
v_00001(Dec 2, 2021)
👋🦊 Xplique is a Python toolkit dedicated to explainability, currently based on Tensorflow.
???? Xplique is a Python toolkit dedicated to explainability, currently based on Tensorflow.
343 Jan 2, 2023
A collection of infrastructure and tools for research in neural network interpretability.
Lucid Lucid is a collection of infrastructure and tools for research in neural network interpretability. We're not currently supporting tensorflow 2!
4.5k Jan 7, 2023
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 (.onnx, .pb, .pbtxt), Keras (.h5, .keras), Tens
20.9k Dec 28, 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,
16.3k Sep 27, 2021
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
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
A library for debugging/inspecting machine learning classifiers and explaining their predictions
ELI5 ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions. It provides support for the following m
2.6k Dec 30, 2022
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
A game theoretic approach to explain the output of any machine learning model.
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allo
18.3k Jan 8, 2023
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 1, 2023
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 9, 2022
A library that implements fairness-aware machine learning algorithms
Themis ML themis-ml is a Python library built on top of pandas and sklearnthat implements fairness-aware machine learning algorithms. Fairness-aware M
105 Dec 30, 2022
JittorVis is a deep neural network computational graph visualization library based on Jittor.
JittorVis - Visual understanding of deep learning model.
131 Jun 25, 2021
GNNLens2 is an interactive visualization tool for graph neural networks (GNN).
GNNLens2 is an interactive visualization tool for graph neural networks (GNN).
143 Jan 7, 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 6, 2022
JittorVis - Visual understanding of deep learning model.
JittorVis is a deep neural network computational graph visualization library based on Jittor.
182 Jan 6, 2023
Portal is the fastest way to load and visualize your deep neural networks on images and videos 🔮
Portal is the fastest way to load and visualize your deep neural networks on images and videos ??
243 Jan 5, 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.
6.5k Jan 1, 2023
Bias and Fairness Audit Toolkit
The Bias and Fairness Audit Toolkit Aequitas is an open-source bias audit toolkit for data scientists, machine learning researchers, and policymakers
513 Jan 6, 2023