Related Issues (16)
- General shape insertion for synthetic datasets.
- GIN general architectures available for both node and graph classification.
- Network statistics as node features in synthetic dataset.
- Node and edge references within a few explainers
- Support for new PyG implementations of GCN in Explainers.
- Define axis of difficulty for the synthetic dataset generation options.
- MUTAG dataset in framework.
- Context drawing around explanations to debug explanation generation in datasets.
- Ground truth explanations for all generation tactics in BAShapes.
- Implement our own version of GCN, GIN that is stable locally.
- Solidify node classification visualization function for Explanation input.
- Fix extract_step function to handle new PyG functionality in a predictable way.
- Fix GNN-LRP HOT 1
- Move functions from in-class for BAShapes to utils functions.
- How to generate EXPS for Real World Data? HOT 2
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