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The official PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf '21)

Home Page: https://arxiv.org/abs/2010.12878

License: GNU General Public License v3.0

Python 100.00%
gcn gnn deepwalk multiplex graph2vec pytorch graph-neural-network deep-learning pathfinder message-passing

pdn's Issues

Use more than one adjacency matrix

Hi there,

I see in your paper, you use sum of more than one adjacency matrix(like different power of the origin matrix), but there is no such operation in code implementation, is there any reason, and does it affect performance?

Multiplex datasets

Hi,

I really like your paper and was more interested in it, so I took into multiplex datasets, and for these two datasets in table 4, it is written that both of them have 2 classes. On the other hand, you have cited DMGI paper as a source of your datasets, but DMGI paper has 3 classes for each of them. Maybe I got something wrong and clarification would help. So, could you please help me with this? Why do you have two classes instead of three and how did you implement this?

Thank you! :)

About dataset and `edge_features`

Hi there!

I have some questions,

  • Could you please tell me which dataset you used in this repo? It doesn't seem to be any dataset in your paper.

  • How was edge_features generated?

Thanks.

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