1. 29 Mar, 2023 1 commit
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  5. 22 Feb, 2023 1 commit
    • Tingyu Wang's avatar
      [Model] Add `dgl.nn.CuGraphSAGEConv` model (#5137) · bcf9923b
      Tingyu Wang authored
      
      
      * add CuGraphSAGEConv model
      
      * fix lint issues
      
      * update model to reflect changes in make_mfg_csr(), move max_in_degree to forward()
      
      * lintrunner
      
      * allow reset_parameters()
      
      * remove norm option, simplify test
      
      * allow full graph fallback option, add example
      
      * address comments
      
      * address reviews
      
      ---------
      Co-authored-by: default avatarMufei Li <mufeili1996@gmail.com>
      bcf9923b
  6. 19 Feb, 2023 2 commits
  7. 15 Feb, 2023 1 commit
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    • Minjie Wang's avatar
      [Sparse][Example] Add TWIRLS example in sparse API (#4922) · c604366d
      Minjie Wang authored
      * add twirls
      
      * update attention part
      
      * update; add val_like to mock_sparse
      
      * black
      c604366d
    • Dylan's avatar
      GCN example correction (#4969) (#4976) · b84de903
      Dylan authored
      Correction like mentioned in #4969 
      
      I noticed that there is a normalisation step on line 97 while the normalised values are not used downstream. Even if this was meant to show the normalisation step, it would not be calculating the normalisation step described in the CGN paper. The paper considers both in and out degrees while the normalisation in the code only describes normalisation using the in degrees.
      In the end, the normalised values are assigned to g.ndata["norm"] but these values are not used afterwards.
      
      Having a normalisation step here is also unnecessary since the GraphConv layer that is used already takes care of the normalisation. https://docs.dgl.ai/en/0.9.x/_modules/dgl/nn/pytorch/conv/graphconv.html#GraphConv
      
      It confused me for a second thinking that I had to do the normalisation myself but this is already handled by the GraphConf.
      b84de903
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