Answers.Body: <p>The answer is Graph Readout operation can get a graph level representation out of the node/edge representations. Read the following:
  <a href="https://lifesci.dgl.ai/api/model.readout.html" rel="nofollow noreferrer">https://lifesci.dgl.ai/api/model.readout.html</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-12-20T09:19:56.740Z
Answers.Text: You might be on to something. However, it is helpful to add to your answer why you think this is a solution and to provide external links with additional explanations. Providing APIs is generally not the best approach for explaining new concepts.
Answers.UserDisplayName: Robin van Hoorn
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-12-26T06:33:00.853Z
Answers.Text: @RobinvanHoorn I will go through the documentation fully and understand it and give an update to my answer with proper explanations. I am not sure who gave a -1 to my answer. What do you mean by 'you might be on to something'?
Answers.UserDisplayName: skumaravel
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-12-26T10:09:52.093Z
Answers.Text: What i meant with 'you might be on to something' is that the API does look like something that can be used. I was thinking along the lines of graph regression/graph classification, and reading a few sentences in the link i feel like its somewhat similar.
Answers.UserDisplayName: Robin van Hoorn
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-12-27T04:54:31.670Z
Answers.Text: @RobinvanHoorn Oh, my bad! I misunderstood it.
Answers.UserDisplayName: skumaravel
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-12-20T04:52:07.030Z
Answers.LastActivityDate: 2022-12-20T04:52:07.030Z
Answers.UserDisplayName: skumaravel
Body: <p>In a knowledge graph, embedding vectors can be learned for nodes (node embedding) and edges (edge embeddings). Is there a method to learn one single embedding vector for the entire knowledge graph?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-12-19T08:15:48.307Z
Comments.Text: Do you mean the entire graph, or rather the entire relationship between two nodes through an edge?
Comments.UserDisplayName: Jaume Oliver Lafont
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-12-19T12:24:45.553Z
Comments.Text: @JaumeOliverLafont The entire graph
Comments.UserDisplayName: skumaravel
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-12-19T13:35:01.093Z
Comments.Text: Im not too familiar with knowledge graphs. But seeing as you talk about embedding vectors for nodes, are the knowledge graphs you talk about embedded using graph neural networks? Because in that case you could learn a graph classification/regression network for embedding the complete graph.
Comments.UserDisplayName: Robin van Hoorn
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-12-19T20:32:58.410Z
Comments.Text: @RobinvanHoorn Yes the nodes are embedded using GNNs. How will learning a graph classification/regression network result in embedding for the entire graph?
Comments.UserDisplayName: skumaravel
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-12-20T09:14:39.253Z
Comments.Text: Well in a GNN you can have node embeddings which are then stacked/aggregated together. On that combined vector you can add a multi-layer perceptron to learn a new embedding on top. I think graph2vec is worth looking into for you.
Comments.UserDisplayName: Robin van Hoorn
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-12-26T06:31:57.157Z
Comments.Text: @RobinvanHoorn Thank you for your answer. I will think over this approach and try to implement it.
Comments.UserDisplayName: skumaravel
Tags: <embeddings><knowledge-graph>
Title: Are there methods which represent entire knowledge graphs in one embedding vector?
UserDisplayName: skumaravel
ThreadText: # Are there methods which represent entire knowledge graphs in one embedding vector?
  
  (Asked by: username_0 on 2022-12-19)
  
  In a knowledge graph, embedding vectors can be learned for nodes (node embedding) and edges (edge embeddings). Is there a method to learn one single embedding vector for the entire knowledge graph?
  
  **Tags:** embeddings, knowledge-graph
  > **Comments:**
  > **username_1**: Do you mean the entire graph, or rather the entire relationship between two nodes through an edge?  
  > **username_0**: @JaumeOliverLafont The entire graph  
  > **username_2**: Im not too familiar with knowledge graphs. But seeing as you talk about embedding vectors for nodes, are the knowledge graphs you talk about embedded using graph neural networks? Because in that case you could learn a graph classification/regression network for embedding the complete graph.  
  > **username_0**: @RobinvanHoorn Yes the nodes are embedded using GNNs. How will learning a graph classification/regression network result in embedding for the entire graph?  
  > **username_2**: Well in a GNN you can have node embeddings which are then stacked/aggregated together. On that combined vector you can add a multi-layer perceptron to learn a new embedding on top. I think graph2vec is worth looking into for you.  
  > **username_0**: @RobinvanHoorn Thank you for your answer. I will think over this approach and try to implement it.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_0 on 2022-12-20. Score: -1
  
  The answer is Graph Readout operation can get a graph level representation out of the node/edge representations. Read the following:
  <https://lifesci.dgl.ai/api/model.readout.html>
  
  > **Comments:**
  > **username_2**: You might be on to something. However, it is helpful to add to your answer why you think this is a solution and to provide external links with additional explanations. Providing APIs is generally not the best approach for explaining new concepts.  
  > **username_0**: @RobinvanHoorn I will go through the documentation fully and understand it and give an update to my answer with proper explanations. I am not sure who gave a -1 to my answer. What do you mean by 'you might be on to something'?  
  > **username_2**: What i meant with 'you might be on to something' is that the API does look like something that can be used. I was thinking along the lines of graph regression/graph classification, and reading a few sentences in the link i feel like its somewhat similar.  
  > **username_0**: @RobinvanHoorn Oh, my bad! I misunderstood it.  
  
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