Answers.Body: <p>Regarding &quot;what the purpose of this is as it shows a table and there are tick marks if checked.&quot;, it sounds like that the tick marks indicating convolution operation support. This means you could train the specific graph with specific convolution method.</p>
  <p>  For example (directly from <a href="https://pytorch-geometric.readthedocs.io/en/latest/cheatsheet/gnn_cheatsheet.html" rel="nofollow noreferrer">same webpage</a>), &quot;SparseTensor&quot;: If checked (✓), supports message passing based on <code>torch_sparse.SparseTensor</code>, e.g., <code>GCNConv(...).forward(x, adj_t)</code>.</p>
  <p>  In other words, &quot;SparseTensor&quot;: If checked (✓), you could run convolution operation using <code>torch_sparse.SparseTensor</code>, e.g., <code>GCNConv(...).forward(x, adj_t)</code></p>
  <p>Here is <a href="https://pytorch-geometric.readthedocs.io/en/latest/notes/create_gnn.html?highlight=message%20passing" rel="nofollow noreferrer">PyTorch own definition of Message Passing</a>: Generalizing the convolution operator to irregular domains is typically expressed as a neighborhood aggregation or message passing scheme.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-09-29T16:03:47.923Z
Answers.LastActivityDate: 2023-09-29T16:21:22.447Z
Answers.LastEditDate: 2023-09-29T16:21:22.447Z
Answers.UserDisplayName: Cloud Cho
Body: <p>I am going through the Pytorch Geometric documentation: <a href="https://pytorch-geometric.readthedocs.io/en/latest/index.html" rel="nofollow noreferrer">https://pytorch-geometric.readthedocs.io/en/latest/index.html</a> which is built on Pytorch .Here they mentioned about GNN Cheatsheet: <a href="https://pytorch-geometric.readthedocs.io/en/latest/cheatsheet/gnn_cheatsheet.html" rel="nofollow noreferrer">https://pytorch-geometric.readthedocs.io/en/latest/cheatsheet/gnn_cheatsheet.html</a>. Can someone explain what the purpose of this is as it shows a table and there are tick marks if checked. Please explain how to understand the first function with the related attributes and then I can proceed further. Thank you.</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-09-29T00:23:39.537Z
Comments.Text: Would put some additional explanation on " to replace the links with text so that people can easily understand."?
Comments.UserDisplayName: Cloud Cho
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-09-29T14:47:51.937Z
Comments.Text: @Cloud Cho  Thanks for your feed back I will look through it please help me in the first question I've asked regaeding GNNsheet.
Comments.UserDisplayName: akhil
Tags: <pytorch><graph-neural-networks><geometric-deep-learning><graph-theory>
Title: What is GNN Cheatsheet in PyG Docs
UserDisplayName: akhil
ThreadText: # What is GNN Cheatsheet in PyG Docs
  
  (Asked by: username_0 on 2023-09-25)
  
  I am going through the Pytorch Geometric documentation: <https://pytorch-geometric.readthedocs.io/en/latest/index.html> which is built on Pytorch .Here they mentioned about GNN Cheatsheet: <https://pytorch-geometric.readthedocs.io/en/latest/cheatsheet/gnn_cheatsheet.html>. Can someone explain what the purpose of this is as it shows a table and there are tick marks if checked. Please explain how to understand the first function with the related attributes and then I can proceed further. Thank you.
  
  **Tags:** pytorch, graph-neural-networks, geometric-deep-learning, graph-theory
  
  > **Comments:**
  > **username_1**: Would put some additional explanation on " to replace the links with text so that people can easily understand."?  
  > **username_0**: @username_1  Thanks for your feed back I will look through it please help me in the first question I've asked regaeding GNNsheet.  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2023-09-29. Score: 1
  
  Regarding "what the purpose of this is as it shows a table and there are tick marks if checked.", it sounds like that the tick marks indicating convolution operation support. This means you could train the specific graph with specific convolution method.
  
    For example (directly from [same webpage](https://pytorch-geometric.readthedocs.io/en/latest/cheatsheet/gnn_cheatsheet.html)), "SparseTensor": If checked (✓), supports message passing based on `torch_sparse.SparseTensor`, e.g., `GCNConv(...).forward(x, adj_t)`.
  
    In other words, "SparseTensor": If checked (✓), you could run convolution operation using `torch_sparse.SparseTensor`, e.g., `GCNConv(...).forward(x, adj_t)`
  
  Here is [PyTorch own definition of Message Passing](https://pytorch-geometric.readthedocs.io/en/latest/notes/create_gnn.html?highlight=message%20passing): Generalizing the convolution operator to irregular domains is typically expressed as a neighborhood aggregation or message passing scheme.
  
  
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