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Explanability for Heterogeneous Graphs #5630

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RexYing opened this issue Oct 7, 2022 · 0 comments
Open
3 tasks

Explanability for Heterogeneous Graphs #5630

RexYing opened this issue Oct 7, 2022 · 0 comments

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@RexYing
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RexYing commented Oct 7, 2022

🚀 Heterogeneous Graph Explanation

Explainability support for heterogeneous graphs. There exist simple ways to adapt current explainability methods into heterogeneous graphs. For example, we can use gradient-based methods by taking gradient with respect to adjacencies (edge_index) of different message types. We can also adapt GNNExplainer by creating a mask corresponding to each of the edge_index for different message types.

Thanks for the good points@Padarn and @wsad1! I'll discuss offline and update this issue with more details for things like HGTConv in a few days.

Tasks:

  • Extension of the current explainability framework to work for HeteroData objects.
  • Incorporate captum-based explainability implementation (such as SHAP, DeepLIFT) for HeteroData.
  • Potential extension to hypergraph use cases (where the graph data is represented as nodes and a set of hyperedges which can connect more than 2 nodes).

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rusty1s added a commit that referenced this issue Nov 4, 2022
…GNNs (#5886)

#5630 First step in
support `to_captum` for heterogenous graphs.

Co-authored-by: rusty1s <matthias.fey@tu-dortmund.de>
JakubPietrakIntel pushed a commit to JakubPietrakIntel/pytorch_geometric that referenced this issue Nov 25, 2022
…GNNs (pyg-team#5886)

pyg-team#5630 First step in
support `to_captum` for heterogenous graphs.

Co-authored-by: rusty1s <matthias.fey@tu-dortmund.de>
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