Implementing XGNN: Towards Model-Level Explanations of Graph Neural Networks

.gitignore

@@ -28,6 +28,5 @@ examples/**/*.png
 examples/**/*.pdf
 benchmark/results/
 .mypy_cache/
-
 !torch_geometric/data/
 !test/data/

CHANGELOG.md

@@ -40,6 +40,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added the `RCDD` dataset ([#8196](https://github.com/pyg-team/pytorch_geometric/pull/8196))
 - Added distributed `GAT + ogbn-products` example targeting XPU device ([#8032](https://github.com/pyg-team/pytorch_geometric/pull/8032))
 - Added the option to skip explanations of certain message passing layers via `conv.explain = False` ([#8216](https://github.com/pyg-team/pytorch_geometric/pull/8216))
+- Added implementation of generative graph explanation to `explain` module (https://github.com/pyg-team/pytorch_geometric/pull/8618)
 
 ### Changed
 

docs/source/modules/explain.rst

@@ -55,6 +55,10 @@ Explanations
    :show-inheritance:
    :members:
 
+.. autoclass:: torch_geometric.explain.GenerativeExplanation
+   :show-inheritance:
+   :members:
+
 Explainer Algorithms
 --------------------