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
Owner
AstraZeneca
Data Science & AI: Unlocking new science insights
AstraZeneca
Pytorch implementation of convolutional neural network visualization techniques

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

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Code for "High-Precision Model-Agnostic Explanations" paper

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