From 6563f66bd1d47ed20b509627931da98a185cdafd Mon Sep 17 00:00:00 2001 From: AtomicVar Date: Fri, 26 Jan 2024 20:18:04 +0800 Subject: [PATCH 1/4] Add READMEs to some examples --- examples/contrib/README.md | 10 ++++++++++ examples/distributed/README.md | 6 ++++++ examples/explain/README.md | 13 +++++++++++++ examples/jit/README.md | 10 ++++++++++ examples/pytorch_ignite/README.md | 7 +++++++ examples/pytorch_lightning/README.md | 9 +++++++++ 6 files changed, 55 insertions(+) create mode 100644 examples/contrib/README.md create mode 100644 examples/distributed/README.md create mode 100644 examples/explain/README.md create mode 100644 examples/jit/README.md create mode 100644 examples/pytorch_ignite/README.md create mode 100644 examples/pytorch_lightning/README.md diff --git a/examples/contrib/README.md b/examples/contrib/README.md new file mode 100644 index 000000000000..4e360c9f488e --- /dev/null +++ b/examples/contrib/README.md @@ -0,0 +1,10 @@ +# Examples for the Contrib Package + +This directory contains examples demonstrating the use of `torch_geometric.contrib` package. The `contrib` package of PyTorch Geometric is a staging area for early-stage, experimental code. Modules included here might be moved to the main library in the future. + +| Example | Description | +| ----------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ | +| [`rbcd_attack_poisoning.py`](./rbcd_attack_poisoning.py) | Demonstrates the RBCD (Resource-Based Critical Data) attack with data poisoning strategies. | +| [`pgm_explainer_node_classification.py`](./pgm_explainer_node_classification.py) | Showcases the PGM (Probabilistic Graphical Model) Explainer for node classification tasks. | +| [`rbcd_attack.py`](./rbcd_attack.py) | Provides an example of an RBCD attack in network settings. | +| [`pgm_explainer_graph_classification.py`](./pgm_explainer_graph_classification.py) | Details the application of the PGM Explainer for graph classification. | diff --git a/examples/distributed/README.md b/examples/distributed/README.md new file mode 100644 index 000000000000..645800ba30d5 --- /dev/null +++ b/examples/distributed/README.md @@ -0,0 +1,6 @@ +# Examples for Distributed Graph Learning + +This directory contains examples for distributed graph learning. The examples are organized into two subdirectories: + +1. `graphlearn_for_pytorch`: Using GraphLearn-for-PyTorch (GLT) for Distributed Training with PyG. +2. `pyg`: Distributed graph learning with PyG's `distributed` package. diff --git a/examples/explain/README.md b/examples/explain/README.md new file mode 100644 index 000000000000..8d44612f8648 --- /dev/null +++ b/examples/explain/README.md @@ -0,0 +1,13 @@ +# Examples for Generating Explanations of Graph Neural Networks + +This directory contains examples demonstrating the use of `torch_geometric.explain` package. The `explain` package of PyTorch Geometric provides a set of tools to explain the predictions of a PyG model or to explain the underlying phenomenon of a dataset. + +| Example | Description | +| -------------------------------------------------- | --------------------------------------------------------------------------------------------- | +| [`captum_explainer.py`](./captum_explainer.py) | Demonstrates the use of Captum-based explainer for explaining Graph Neural Networks (GNN) model in node classification. | +| [`captum_explainer_hetero_link.py`](./captum_explainer_hetero_link.py) | Demonstrates the use of Captum-based explainer for explaining GNN model in heterogenous link prediction. | +| [`gnn_explainer.py`](./gnn_explainer.py) | Shows GNNExplainer applied to the Cora dataset for understanding GNN model. | +| [`gnn_explainer_ba_shapes.py`](./gnn_explainer_ba_shapes.py) | Shows GNNExplainer applied to the BA-Shapes dataset for understanding GNN model. | +| [`gnn_explainer_link_pred.py`](./gnn_explainer_link_pred.py) | Demonstrates the use of GNNExplainer for explaining GNN model in link prediction. | +| [`graphmask_explainer.py`](./graphmask_explainer.py) | Demonstrates the use of GraphMask-Explainer for explaining GNN model in node classification. | + diff --git a/examples/jit/README.md b/examples/jit/README.md new file mode 100644 index 000000000000..e21c0d1b0a86 --- /dev/null +++ b/examples/jit/README.md @@ -0,0 +1,10 @@ +# JIT Examples for Graph Neural Networks + +This directory contains examples demonstrating the use of Just-In-Time (JIT) compilation in different Graph Neural Network (GNN) models. + +| Example | Description | +| ---------------------- | ----------------------------------------------------------------------------------------------- | +| [`film.py`](./film.py) | Demonstrates the use of JIT compilation in [GNN-FiLM](https://arxiv.org/abs/1906.12192). | +| [`gat.py`](./gat.py) | Demonstrates the use of JIT compilation in GAT. | +| [`gcn.py`](./gcn.py) | Demonstrates the use of JIT compilation in GCN. | +| [`gin.py`](./gin.py) | Demonstrates the use of JIT compilation in GIN. | diff --git a/examples/pytorch_ignite/README.md b/examples/pytorch_ignite/README.md new file mode 100644 index 000000000000..a3f450e28be2 --- /dev/null +++ b/examples/pytorch_ignite/README.md @@ -0,0 +1,7 @@ +# Examples for PyTorch Ignite + +This directory demonstrates the application of PyTorch Ignite in the context of Graph Neural Networks (GNNs). + +| Example | Description | +| ------------------------------------ | ------------------------------------------------------------------------------------------------- | +| [`gin.py`](./gin.py) | This script demonstrates how to implement the Graph Isomorphism Network (GIN) model using PyTorch Ignite. | diff --git a/examples/pytorch_lightning/README.md b/examples/pytorch_lightning/README.md new file mode 100644 index 000000000000..6dc0061e6de6 --- /dev/null +++ b/examples/pytorch_lightning/README.md @@ -0,0 +1,9 @@ +# Examples for PyTorch Lightning + +This directory provides examples showcasing the integration of PyTorch Geometric with [PyTorch Lightning](https://github.com/Lightning-AI/pytorch-lightning). + +| Example | Description | +| ------------------------------------ | ------------------------------------------------------------------------------------------------- | +| [`gin.py`](./gin.py) | Demonstrates how to combine PyTorch Geometric and PyTorch Lightning for implementing the Graph Isomorphism Network (GIN) model. | +| [`graph_sage.py`](./graph_sage.py) | Demonstrates how to combine PyTorch Geometric and PyTorch Lightning for implementing the GraphSAGE model, suitable for scalable graph-structured data processing. | +| [`relational_gnn.py`](./relational_gnn.py) | Demonstrates the use of PyTorch Geometric and PyTorch Lightning for building a Relational Graph Neural Network, emphasizing handling relational data in graphs. | From f63f43d6e5aceacd23ec1132a4a0af173c4e2410 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 26 Jan 2024 12:22:44 +0000 Subject: [PATCH 2/4] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- examples/contrib/README.md | 12 ++++++------ examples/distributed/README.md | 2 +- examples/explain/README.md | 17 ++++++++--------- examples/jit/README.md | 10 +++++----- examples/pytorch_ignite/README.md | 6 +++--- examples/pytorch_lightning/README.md | 10 +++++----- 6 files changed, 28 insertions(+), 29 deletions(-) diff --git a/examples/contrib/README.md b/examples/contrib/README.md index 4e360c9f488e..9e038b46b6ad 100644 --- a/examples/contrib/README.md +++ b/examples/contrib/README.md @@ -2,9 +2,9 @@ This directory contains examples demonstrating the use of `torch_geometric.contrib` package. The `contrib` package of PyTorch Geometric is a staging area for early-stage, experimental code. Modules included here might be moved to the main library in the future. -| Example | Description | -| ----------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------ | -| [`rbcd_attack_poisoning.py`](./rbcd_attack_poisoning.py) | Demonstrates the RBCD (Resource-Based Critical Data) attack with data poisoning strategies. | -| [`pgm_explainer_node_classification.py`](./pgm_explainer_node_classification.py) | Showcases the PGM (Probabilistic Graphical Model) Explainer for node classification tasks. | -| [`rbcd_attack.py`](./rbcd_attack.py) | Provides an example of an RBCD attack in network settings. | -| [`pgm_explainer_graph_classification.py`](./pgm_explainer_graph_classification.py) | Details the application of the PGM Explainer for graph classification. | +| Example | Description | +| ---------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | +| [`rbcd_attack_poisoning.py`](./rbcd_attack_poisoning.py) | Demonstrates the RBCD (Resource-Based Critical Data) attack with data poisoning strategies. | +| [`pgm_explainer_node_classification.py`](./pgm_explainer_node_classification.py) | Showcases the PGM (Probabilistic Graphical Model) Explainer for node classification tasks. | +| [`rbcd_attack.py`](./rbcd_attack.py) | Provides an example of an RBCD attack in network settings. | +| [`pgm_explainer_graph_classification.py`](./pgm_explainer_graph_classification.py) | Details the application of the PGM Explainer for graph classification. | diff --git a/examples/distributed/README.md b/examples/distributed/README.md index 645800ba30d5..a570cadca28b 100644 --- a/examples/distributed/README.md +++ b/examples/distributed/README.md @@ -3,4 +3,4 @@ This directory contains examples for distributed graph learning. The examples are organized into two subdirectories: 1. `graphlearn_for_pytorch`: Using GraphLearn-for-PyTorch (GLT) for Distributed Training with PyG. -2. `pyg`: Distributed graph learning with PyG's `distributed` package. +1. `pyg`: Distributed graph learning with PyG's `distributed` package. diff --git a/examples/explain/README.md b/examples/explain/README.md index 8d44612f8648..6b29785c53ff 100644 --- a/examples/explain/README.md +++ b/examples/explain/README.md @@ -2,12 +2,11 @@ This directory contains examples demonstrating the use of `torch_geometric.explain` package. The `explain` package of PyTorch Geometric provides a set of tools to explain the predictions of a PyG model or to explain the underlying phenomenon of a dataset. -| Example | Description | -| -------------------------------------------------- | --------------------------------------------------------------------------------------------- | -| [`captum_explainer.py`](./captum_explainer.py) | Demonstrates the use of Captum-based explainer for explaining Graph Neural Networks (GNN) model in node classification. | -| [`captum_explainer_hetero_link.py`](./captum_explainer_hetero_link.py) | Demonstrates the use of Captum-based explainer for explaining GNN model in heterogenous link prediction. | -| [`gnn_explainer.py`](./gnn_explainer.py) | Shows GNNExplainer applied to the Cora dataset for understanding GNN model. | -| [`gnn_explainer_ba_shapes.py`](./gnn_explainer_ba_shapes.py) | Shows GNNExplainer applied to the BA-Shapes dataset for understanding GNN model. | -| [`gnn_explainer_link_pred.py`](./gnn_explainer_link_pred.py) | Demonstrates the use of GNNExplainer for explaining GNN model in link prediction. | -| [`graphmask_explainer.py`](./graphmask_explainer.py) | Demonstrates the use of GraphMask-Explainer for explaining GNN model in node classification. | - +| Example | Description | +| ---------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- | +| [`captum_explainer.py`](./captum_explainer.py) | Demonstrates the use of Captum-based explainer for explaining Graph Neural Networks (GNN) model in node classification. | +| [`captum_explainer_hetero_link.py`](./captum_explainer_hetero_link.py) | Demonstrates the use of Captum-based explainer for explaining GNN model in heterogenous link prediction. | +| [`gnn_explainer.py`](./gnn_explainer.py) | Shows GNNExplainer applied to the Cora dataset for understanding GNN model. | +| [`gnn_explainer_ba_shapes.py`](./gnn_explainer_ba_shapes.py) | Shows GNNExplainer applied to the BA-Shapes dataset for understanding GNN model. | +| [`gnn_explainer_link_pred.py`](./gnn_explainer_link_pred.py) | Demonstrates the use of GNNExplainer for explaining GNN model in link prediction. | +| [`graphmask_explainer.py`](./graphmask_explainer.py) | Demonstrates the use of GraphMask-Explainer for explaining GNN model in node classification. | diff --git a/examples/jit/README.md b/examples/jit/README.md index e21c0d1b0a86..3c81d4bdc979 100644 --- a/examples/jit/README.md +++ b/examples/jit/README.md @@ -2,9 +2,9 @@ This directory contains examples demonstrating the use of Just-In-Time (JIT) compilation in different Graph Neural Network (GNN) models. -| Example | Description | -| ---------------------- | ----------------------------------------------------------------------------------------------- | +| Example | Description | +| ---------------------- | ---------------------------------------------------------------------------------------- | | [`film.py`](./film.py) | Demonstrates the use of JIT compilation in [GNN-FiLM](https://arxiv.org/abs/1906.12192). | -| [`gat.py`](./gat.py) | Demonstrates the use of JIT compilation in GAT. | -| [`gcn.py`](./gcn.py) | Demonstrates the use of JIT compilation in GCN. | -| [`gin.py`](./gin.py) | Demonstrates the use of JIT compilation in GIN. | +| [`gat.py`](./gat.py) | Demonstrates the use of JIT compilation in GAT. | +| [`gcn.py`](./gcn.py) | Demonstrates the use of JIT compilation in GCN. | +| [`gin.py`](./gin.py) | Demonstrates the use of JIT compilation in GIN. | diff --git a/examples/pytorch_ignite/README.md b/examples/pytorch_ignite/README.md index a3f450e28be2..2700622d7c30 100644 --- a/examples/pytorch_ignite/README.md +++ b/examples/pytorch_ignite/README.md @@ -2,6 +2,6 @@ This directory demonstrates the application of PyTorch Ignite in the context of Graph Neural Networks (GNNs). -| Example | Description | -| ------------------------------------ | ------------------------------------------------------------------------------------------------- | -| [`gin.py`](./gin.py) | This script demonstrates how to implement the Graph Isomorphism Network (GIN) model using PyTorch Ignite. | +| Example | Description | +| -------------------- | --------------------------------------------------------------------------------------------------------- | +| [`gin.py`](./gin.py) | This script demonstrates how to implement the Graph Isomorphism Network (GIN) model using PyTorch Ignite. | diff --git a/examples/pytorch_lightning/README.md b/examples/pytorch_lightning/README.md index 6dc0061e6de6..9bf9fce5b06a 100644 --- a/examples/pytorch_lightning/README.md +++ b/examples/pytorch_lightning/README.md @@ -2,8 +2,8 @@ This directory provides examples showcasing the integration of PyTorch Geometric with [PyTorch Lightning](https://github.com/Lightning-AI/pytorch-lightning). -| Example | Description | -| ------------------------------------ | ------------------------------------------------------------------------------------------------- | -| [`gin.py`](./gin.py) | Demonstrates how to combine PyTorch Geometric and PyTorch Lightning for implementing the Graph Isomorphism Network (GIN) model. | -| [`graph_sage.py`](./graph_sage.py) | Demonstrates how to combine PyTorch Geometric and PyTorch Lightning for implementing the GraphSAGE model, suitable for scalable graph-structured data processing. | -| [`relational_gnn.py`](./relational_gnn.py) | Demonstrates the use of PyTorch Geometric and PyTorch Lightning for building a Relational Graph Neural Network, emphasizing handling relational data in graphs. | +| Example | Description | +| ------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| [`gin.py`](./gin.py) | Demonstrates how to combine PyTorch Geometric and PyTorch Lightning for implementing the Graph Isomorphism Network (GIN) model. | +| [`graph_sage.py`](./graph_sage.py) | Demonstrates how to combine PyTorch Geometric and PyTorch Lightning for implementing the GraphSAGE model, suitable for scalable graph-structured data processing. | +| [`relational_gnn.py`](./relational_gnn.py) | Demonstrates the use of PyTorch Geometric and PyTorch Lightning for building a Relational Graph Neural Network, emphasizing handling relational data in graphs. | From b9e39216fc0036d88de823ca0171b6b40e14c65f Mon Sep 17 00:00:00 2001 From: rusty1s Date: Mon, 29 Jan 2024 10:00:50 +0000 Subject: [PATCH 3/4] update --- examples/contrib/README.md | 14 ++++++++------ examples/distributed/README.md | 7 ++++--- examples/explain/README.md | 19 ++++++++++--------- examples/jit/README.md | 16 ++++++++-------- examples/pytorch_ignite/README.md | 8 ++++---- examples/pytorch_lightning/README.md | 12 ++++++------ 6 files changed, 40 insertions(+), 36 deletions(-) diff --git a/examples/contrib/README.md b/examples/contrib/README.md index 9e038b46b6ad..3a45117741d2 100644 --- a/examples/contrib/README.md +++ b/examples/contrib/README.md @@ -1,10 +1,12 @@ -# Examples for the Contrib Package +# Examples for External Contributions -This directory contains examples demonstrating the use of `torch_geometric.contrib` package. The `contrib` package of PyTorch Geometric is a staging area for early-stage, experimental code. Modules included here might be moved to the main library in the future. +This directory contains examples demonstrating functionality included in the `torch_geometric.contrib` package. +The `contrib` package of PyG is a staging area for early-stage, experimental code. +Modules included here might be moved to the main library in the future. | Example | Description | | ---------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | -| [`rbcd_attack_poisoning.py`](./rbcd_attack_poisoning.py) | Demonstrates the RBCD (Resource-Based Critical Data) attack with data poisoning strategies. | -| [`pgm_explainer_node_classification.py`](./pgm_explainer_node_classification.py) | Showcases the PGM (Probabilistic Graphical Model) Explainer for node classification tasks. | -| [`rbcd_attack.py`](./rbcd_attack.py) | Provides an example of an RBCD attack in network settings. | -| [`pgm_explainer_graph_classification.py`](./pgm_explainer_graph_classification.py) | Details the application of the PGM Explainer for graph classification. | +| [`rbcd_attack.py`](./rbcd_attack.py) | An example of the RBCD (Resource-based Critical Data) attack | +| [`rbcd_attack_poisoning.py`](./rbcd_attack_poisoning.py) | An example of the RBCD (Resource-Based Critical Data) attack with data poisoning strategies | +| [`pgm_explainer_node_classification.py`](./pgm_explainer_node_classification.py) | An example of the PGM (Probabilistic Graphical Model) explainer for node classification | +| [`pgm_explainer_graph_classification.py`](./pgm_explainer_graph_classification.py) | An example of the PGM (Probabilistic Graphical Model) explainer for graph classification | diff --git a/examples/distributed/README.md b/examples/distributed/README.md index a570cadca28b..5f6e928d0bf3 100644 --- a/examples/distributed/README.md +++ b/examples/distributed/README.md @@ -1,6 +1,7 @@ # Examples for Distributed Graph Learning -This directory contains examples for distributed graph learning. The examples are organized into two subdirectories: +This directory contains examples for distributed graph learning. +The examples are organized into two subdirectories: -1. `graphlearn_for_pytorch`: Using GraphLearn-for-PyTorch (GLT) for Distributed Training with PyG. -1. `pyg`: Distributed graph learning with PyG's `distributed` package. +1. `pyg`: Distributed training via PyG's own `torch_geometric.distributed` package. +1. `graphlearn_for_pytorch`: Distributed training via the external GraphLearn-for-PyTorch (GLT) package. diff --git a/examples/explain/README.md b/examples/explain/README.md index 6b29785c53ff..73f0275499f2 100644 --- a/examples/explain/README.md +++ b/examples/explain/README.md @@ -1,12 +1,13 @@ # Examples for Generating Explanations of Graph Neural Networks -This directory contains examples demonstrating the use of `torch_geometric.explain` package. The `explain` package of PyTorch Geometric provides a set of tools to explain the predictions of a PyG model or to explain the underlying phenomenon of a dataset. +This directory contains examples demonstrating the use of the `torch_geometric.explain` package. +The `explain` package of PyG provides a set of tools to explain the predictions of a GNN model or to explain the underlying phenomenon of a dataset. -| Example | Description | -| ---------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------- | -| [`captum_explainer.py`](./captum_explainer.py) | Demonstrates the use of Captum-based explainer for explaining Graph Neural Networks (GNN) model in node classification. | -| [`captum_explainer_hetero_link.py`](./captum_explainer_hetero_link.py) | Demonstrates the use of Captum-based explainer for explaining GNN model in heterogenous link prediction. | -| [`gnn_explainer.py`](./gnn_explainer.py) | Shows GNNExplainer applied to the Cora dataset for understanding GNN model. | -| [`gnn_explainer_ba_shapes.py`](./gnn_explainer_ba_shapes.py) | Shows GNNExplainer applied to the BA-Shapes dataset for understanding GNN model. | -| [`gnn_explainer_link_pred.py`](./gnn_explainer_link_pred.py) | Demonstrates the use of GNNExplainer for explaining GNN model in link prediction. | -| [`graphmask_explainer.py`](./graphmask_explainer.py) | Demonstrates the use of GraphMask-Explainer for explaining GNN model in node classification. | +| Example | Description | +| ---------------------------------------------------------------------- | ----------------------------------------------------------- | +| [`gnn_explainer.py`](./gnn_explainer.py) | `GNNExplainer` for node classification | +| [`gnn_explainer_link_pred.py`](./gnn_explainer_link_pred.py) | `GNNExplainer` for link prediction | +| [`gnn_explainer_ba_shapes.py`](./gnn_explainer_ba_shapes.py) | `GNNExplainer` applied on the `BAShapes` dataset | +| [`captum_explainer.py`](./captum_explainer.py) | Captum-based explainer for for node classification | +| [`captum_explainer_hetero_link.py`](./captum_explainer_hetero_link.py) | Captum-based explainer for for heterogenous link prediction | +| [`graphmask_explainer.py`](./graphmask_explainer.py) | `GraphMaskExplainer` for node classification | diff --git a/examples/jit/README.md b/examples/jit/README.md index 3c81d4bdc979..232976619c2f 100644 --- a/examples/jit/README.md +++ b/examples/jit/README.md @@ -1,10 +1,10 @@ -# JIT Examples for Graph Neural Networks +# JIT Examples -This directory contains examples demonstrating the use of Just-In-Time (JIT) compilation in different Graph Neural Network (GNN) models. +This directory contains examples demonstrating the use of Just-In-Time (JIT) compilation in different GNN models. -| Example | Description | -| ---------------------- | ---------------------------------------------------------------------------------------- | -| [`film.py`](./film.py) | Demonstrates the use of JIT compilation in [GNN-FiLM](https://arxiv.org/abs/1906.12192). | -| [`gat.py`](./gat.py) | Demonstrates the use of JIT compilation in GAT. | -| [`gcn.py`](./gcn.py) | Demonstrates the use of JIT compilation in GCN. | -| [`gin.py`](./gin.py) | Demonstrates the use of JIT compilation in GIN. | +| Example | Description | +| ---------------------- | ----------------------------------------------------------------- | +| [`gcn.py`](./gcn.py) | JIT compilation in `GCN` | +| [`gat.py`](./gat.py) | JIT compilation in `GAT` | +| [`gin.py`](./gin.py) | JIT compilation in `GIN` | +| [`film.py`](./film.py) | JIT compilation in [`GNN-FiLM`](https://arxiv.org/abs/1906.12192) | diff --git a/examples/pytorch_ignite/README.md b/examples/pytorch_ignite/README.md index 2700622d7c30..0cd789b3f6bc 100644 --- a/examples/pytorch_ignite/README.md +++ b/examples/pytorch_ignite/README.md @@ -1,7 +1,7 @@ # Examples for PyTorch Ignite -This directory demonstrates the application of PyTorch Ignite in the context of Graph Neural Networks (GNNs). +This directory provides examples showcasing the integration of PyG with [PyTorch Ingite](https://pytorch.org/ignite/index.html). -| Example | Description | -| -------------------- | --------------------------------------------------------------------------------------------------------- | -| [`gin.py`](./gin.py) | This script demonstrates how to implement the Graph Isomorphism Network (GIN) model using PyTorch Ignite. | +| Example | Description | +| -------------------- | ---------------------------------------------------------------- | +| [`gin.py`](./gin.py) | Demonstrates how to implement the GIN model using PyTorch Ignite | diff --git a/examples/pytorch_lightning/README.md b/examples/pytorch_lightning/README.md index 9bf9fce5b06a..4c9dbf8e7936 100644 --- a/examples/pytorch_lightning/README.md +++ b/examples/pytorch_lightning/README.md @@ -1,9 +1,9 @@ # Examples for PyTorch Lightning -This directory provides examples showcasing the integration of PyTorch Geometric with [PyTorch Lightning](https://github.com/Lightning-AI/pytorch-lightning). +This directory provides examples showcasing the integration of PyG with [PyTorch Lightning](https://github.com/Lightning-AI/pytorch-lightning). -| Example | Description | -| ------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- | -| [`gin.py`](./gin.py) | Demonstrates how to combine PyTorch Geometric and PyTorch Lightning for implementing the Graph Isomorphism Network (GIN) model. | -| [`graph_sage.py`](./graph_sage.py) | Demonstrates how to combine PyTorch Geometric and PyTorch Lightning for implementing the GraphSAGE model, suitable for scalable graph-structured data processing. | -| [`relational_gnn.py`](./relational_gnn.py) | Demonstrates the use of PyTorch Geometric and PyTorch Lightning for building a Relational Graph Neural Network, emphasizing handling relational data in graphs. | +| Example | Description | +| ------------------------------------------ | ------------------------------------------------------------------------------------ | +| [`graph_sage.py`](./graph_sage.py) | Combines PyG and PyTorch Lightning for node classification via the `GraphSAGE` model | +| [`gin.py`](./gin.py) | Combines PyG and PyTorch Lightning for graph classification via the `GIN` model | +| [`relational_gnn.py`](./relational_gnn.py) | Combines PyG and PyTorch Lightning for heterogeneous node classification | From fafedec6bc7975ae53ff992baa9a7964adc4209f Mon Sep 17 00:00:00 2001 From: rusty1s Date: Mon, 29 Jan 2024 10:02:52 +0000 Subject: [PATCH 4/4] update --- examples/explain/README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/examples/explain/README.md b/examples/explain/README.md index 73f0275499f2..c91db8120808 100644 --- a/examples/explain/README.md +++ b/examples/explain/README.md @@ -3,11 +3,11 @@ This directory contains examples demonstrating the use of the `torch_geometric.explain` package. The `explain` package of PyG provides a set of tools to explain the predictions of a GNN model or to explain the underlying phenomenon of a dataset. -| Example | Description | -| ---------------------------------------------------------------------- | ----------------------------------------------------------- | -| [`gnn_explainer.py`](./gnn_explainer.py) | `GNNExplainer` for node classification | -| [`gnn_explainer_link_pred.py`](./gnn_explainer_link_pred.py) | `GNNExplainer` for link prediction | -| [`gnn_explainer_ba_shapes.py`](./gnn_explainer_ba_shapes.py) | `GNNExplainer` applied on the `BAShapes` dataset | -| [`captum_explainer.py`](./captum_explainer.py) | Captum-based explainer for for node classification | -| [`captum_explainer_hetero_link.py`](./captum_explainer_hetero_link.py) | Captum-based explainer for for heterogenous link prediction | -| [`graphmask_explainer.py`](./graphmask_explainer.py) | `GraphMaskExplainer` for node classification | +| Example | Description | +| ---------------------------------------------------------------------- | ------------------------------------------------------- | +| [`gnn_explainer.py`](./gnn_explainer.py) | `GNNExplainer` for node classification | +| [`gnn_explainer_link_pred.py`](./gnn_explainer_link_pred.py) | `GNNExplainer` for link prediction | +| [`gnn_explainer_ba_shapes.py`](./gnn_explainer_ba_shapes.py) | `GNNExplainer` applied on the `BAShapes` dataset | +| [`captum_explainer.py`](./captum_explainer.py) | Captum-based explainer for node classification | +| [`captum_explainer_hetero_link.py`](./captum_explainer_hetero_link.py) | Captum-based explainer for heterogenous link prediction | +| [`graphmask_explainer.py`](./graphmask_explainer.py) | `GraphMaskExplainer` for node classification |