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

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  • Add new citation: Numeroso et al.

    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

    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

    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

    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

    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
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Implementation of linear CorEx and temporal CorEx.

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A data-driven approach to quantify the value of classifiers in a machine learning ensemble.

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

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Chandan Singh 111 Jan 03, 2023
⬛ Python Individual Conditional Expectation Plot Toolbox

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Austin Rochford 140 Dec 30, 2022
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Lim Swee Kiat 520 Dec 26, 2022
👋🦊 Xplique is a Python toolkit dedicated to explainability, currently based on Tensorflow.

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DEEL 343 Jan 02, 2023
pytorch implementation of "Distilling a Neural Network Into a Soft Decision Tree"

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

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Lutz Roeder 20.9k Dec 28, 2022
Logging MXNet data for visualization in TensorBoard.

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Model analysis tools for TensorFlow

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A python library for decision tree visualization and model interpretation.

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🎆 A visualization of the CapsNet layers to better understand how it works

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Nick Bourdakos 387 Dec 06, 2022
Lime: Explaining the predictions of any machine learning classifier

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A game theoretic approach to explain the output of any machine learning model.

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python partial dependence plot toolbox

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