tech-srl / bottleneck Goto Github PK

Dear tech-srl,
maybe hear is a mistake.

Hi Uri,

Thank you for this amazing and insightful work. I have the following two questions about the experiment. Hope I can get your help.

(1) Did you run experiments for GNNs+FA on the NeighborsMatch dataset? If yes, could you share the results?

(2) I didn't find the results for GIN on the NeighborsMatch dataset when r=7/8. Is there any reason about this?

Best,
Meng

i am confused for the input data of GIN model code part:
x, edge_index, batch = data.x, data.edge_index, data.batch
could you explain the three ones?

Hi Uri,

I hope you're doing well. Your paper is really nice, congrats.

I just have a small question. If I correctly understood your code, you're generating several trees and then you stack them to train in a batch fashion. What I don't understand is why you add self loops to all nodes. The illustrations in the paper don't show any self loops. I guess the only difference is that you'll be using the own embedding of each node when performing message passing, isn't it? Or is there something I'm missing?

Thank you very much for releasing the code :)

Hi,

This is an interesting paper. may find some fundamental issues about GNN.

I have a question about the Tree-NeighborsMatch problem. In my understanding, 1-layer GNN can pretty much solve it, because what the model has to do is:

1. let all the green nodes, including both the target node and labelled leaves nodes, count the number of neighbors they have.
2. Let the classifier at the final layer figure out 2 neighbors means to label "C". then it should label the target node as "C" given that it has 2 neighbors.

In this case, it doesn't have to message pass all the information from leaves nodes to target nodes to find out the most similar one. It doesn't need to "match", it just needs to learn 2 --> "C"

Hi, Uri Alon, I love this work. I think your paper is splendid and innovative to follow. And I want to know whether and where this paper is published. I can't wait to cite your paper as my support.

Hello,

Thanks for this great repo. I cannot reproduce the exact results with the default configurations as follows:

python run-gcn-2-8.py
python run-gat-2-8.py
python run-ggnn-2-8.py
python run-gin-2-8.py

The results produced by these scripts as as follows:

Marker * denotes the experiments are still running.

May I ask the hyper-parameters in the default configurations are the same as the paper used？

Thanks.