Body: <p>When you applying a graph structured data to the graph convolution network,  what are the benefits of using the state information that maintains the graph structure?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-27T11:36:27.783Z
Comments.Text: By _state information_, do you mean the vector representing the state of each node and, possibly, the vector representing the whole graph?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-27T16:44:20.023Z
Comments.Text: @nbro yes, the vector representing the state of such nodes information
Comments.UserDisplayName: unsmoother
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-27T17:42:09.800Z
Comments.Text: Is there any specific GDL architecture you're referring to?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-27T23:54:15.577Z
Comments.Text: @nbro I recently read https://arxiv.org/abs/1611.08402. Although the method is compared with various GCNs methods, the method presented in this (MoNet) paper is very good. Is this because you are able to use such information better than other methods?
Comments.UserDisplayName: unsmoother
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-06-28T15:04:44.663Z
Comments.Text: It is difficult to explain the exact reason behind the better results of MoNet w.r.t. some previous models. They use some learnable parameters, rather than handcrafting them, and they say that MoNet generalises other previous works. This might affect the performance of the model. Regarding your original question, I can try to provide an answer, but I am not sure my answer will address your issue. It's just my interpretation.
Comments.UserDisplayName: nbro
Tags: <machine-learning><deep-learning><graphs><geometric-deep-learning><graph-theory>
Title: What are the benefits of using the state information that maintains the graph structure?
UserDisplayName: unsmoother
ThreadText: # What are the benefits of using the state information that maintains the graph structure?
  
  (Asked by: username_0 on 2019-06-27)
  
  When you applying a graph structured data to the graph convolution network, what are the benefits of using the state information that maintains the graph structure?
  
  **Tags:** machine-learning, deep-learning, graphs, geometric-deep-learning, graph-theory
  
  > **Comments:**
  > **username_1**: By _state information_, do you mean the vector representing the state of each node and, possibly, the vector representing the whole graph?  
  > **username_0**: @username_1 yes, the vector representing the state of such nodes information  
  > **username_1**: Is there any specific GDL architecture you're referring to?  
  > **username_0**: @username_1 I recently read https://arxiv.org/abs/1611.08402. Although the method is compared with various GCNs methods, the method presented in this (MoNet) paper is very good. Is this because you are able to use such information better than other methods?  
  > **username_1**: It is difficult to explain the exact reason behind the better results of MoNet w.r.t. some previous models. They use some learnable parameters, rather than handcrafting them, and they say that MoNet generalises other previous works. This might affect the performance of the model. Regarding your original question, I can try to provide an answer, but I am not sure my answer will address your issue. It's just my interpretation.  
  
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  ## 0 answers
  
  