Implementation for On Provable Benefits of Depth in Training Graph Convolutional Networks

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Deep Learning DGCN
Overview

Implementation for On Provable Benefits of Depth in Training Graph Convolutional Networks

Setup

This implementation is based on PyTorch >= 1.0.0. Small dataset (including Cora, Citeseer, and Pubmed) are located in the data folder.

Usage

Please use the jupyter notebook files localed in the ./jupyter_notebooks. Please copy the file you want to run to the root folder, i.e., ./DGCN/CODE_WANT_TO_RUN.ipynb, then directly run it using jupyter notebook.

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Comments
  • Implementation of APPNP depart from original design

    Implementation of APPNP depart from original design

    Hi,

    I just find that the implementation of APPNP is not the same as the standard design.

    https://github.com/CongWeilin/DGCN/blob/master/model.py#L243

    In your implementation, the node features only go through a linear transform (self.linear_in), then apply PPR propagation. Finally, it followed by another linear transformation (self.linear_out).

    However, this departs from the original APPNP design.

    https://github.com/klicperajo/ppnp/blob/master/ppnp/pytorch/ppnp.py

    In the original design, the authors of APPNP first transform node features into C dimensional hidden representation using MLP (C is the number of classes). Then they propagate based on PPR. We can also clearly see from their illustration figure in https://github.com/klicperajo/ppnp that your implementation is not the same. I wonder how will this affect the results in your paper?

    Thanks, Eli

    opened by elichienxD 4
  • Decoupled GCN not provided

    Decoupled GCN not provided

    Hi, I found you provide Decoupled Conv in layers.py, but the models.py do not contain the code of DGCN model. Also, is \beta trainable, or is it a hyperparameter?

    opened by jerrychen-official 1
Owner
Weilin Cong
Graduate student at Pennsylvania State University.
Weilin Cong
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