weihua916 / powerful-gnns Goto Github PK

Hi, I used the same codes and datasets to tune the parameters provided by the paper. The random seed is set by 0. The followings are the results:

Where the first line represents results from the paper and the second line represents experimental results I conducted.
As you can see, I can not reproduce the results of the paper on many datasets. Would you tell me how to reproduce your results?

I am a beginner. I want to ask you how you got the result of your paper.The results of each validation are the max, and then the max in 10?

Hi,
I want to use your GIN implementation for my own dataset. I don't understand how should I prepare the initial txt file for my dataset.
can you explain it, please.
Thanks

Hello,weihua,thanks for your sharing this code.I just started to touch the knowledge of the graph neural network,soI am not very clear about something. I would like to ask what is the MUTAG data set.Like what's the nodes or edges.etc.Thanks^_^

Hi:
I use your published of this paper, I can't reproduce the result. For example, MUTAG, test accuracy is very low, about 70 percent while train accuracy is near to 1. There occurs overfitting I think. Do you meet this before and how to fix it ?

Hi. Thanks for great work.
I have a question about input data, specifically for node id numbering.
Is a node id unique across all graphs? or unique in a graph?

I mean, graph 1 contains node A, B, C, and graph 2 contain node A, D, E.
Then, node ids for graph 1 and 2 are both node 1, 2, 3 (for inductive setting).
If transductive setting, node ids are 1, 2, 3 for graph1 and 1, 4, 5 for graph2.

Is it correct?

Hello, your program only uses the node label as input. If you want to add the node attributes as input, how should the program deal with the label

Thank you for the great work!

I would like to ask the correct hyperparameters for each of the datasets in order to replicate the paper reported result. Thank you!

Hi ! I am interested in your GIN work, but can you provide a spectral view of GIN's operation like GCN?

Hi, thank you for such an impressive work. I would like to apply your algorithm to my task, so I need to create dataset which will fit your code. I have, say, 150 graphs of 200 nodes each, where all the nodes are equal.


I'm trying to understand your txt files, but have some issues with that.
For example:

10 0
0 3 1 2 9
0 3 0 2 9
0 4 0 1 3 9
0 3 2 4 5
0 3 3 5 6
0 5 3 4 6 7 8
0 4 4 5 7 8
0 3 5 6 8
0 3 5 6 7
1 3 0 1 2

My assumption is that the block correspond to a graph, 10 is a number of nodes while 0 is a graph class label. Each row correspond to a node, where first value is a (node label I guess?), second is a number of links, and other are connections. Is that right? Is row number correspond to the node index?

Dear Author,

I could reproduce task including MYTAG,PTC,NCl1,PROTEINS,IMDB-B,IMDB-M,RDT-M5K using the parameters of your paper, however, I could not reproduce the results for RDT-B and COLLAB using the parameters.

Might I get your advice or suggestion about reproducing the results of COLLAB and RDT-B.

Regards

I don't see you using LIB-SVM in the source code, so it's meaning that the cross-validation strategy of LIB-SVM is used? I.e. the one you wrote in the README?

Hi and thanks for sharing your code.
When applying GIN to node classification task for example on cora dataset, the accuracy is low.
You said in the paper that for mean aggregation and linear function GIN is GCN. I use the DGL implementation of GIN for node classification but I can't produce accuracy near to GCN.
IS there a need for some preprocessing when applying GIN on node classification?

Hello authors,

Thank you for the great work!

I would like to know about the information of discrete labels for all datasets.

In each dataset, I can only see the ".txt" file which includes only discrete numbers. Therefore, it's hard for me to guess which category belongs to what chemical element.

Thank you,

I used raw code / pyg /dgl to reproduce experiments on COLLAB and the accuracy was always below 70%.

Thank you for your work.
You have used dropout prior to computing the output from each layer. What is the role of this dropout?
See:

Hi,
I'm looking at Table 1 in the paper, and the number of classes associated with the datasets does not match the description of the data in the appendix. For example, the MUTAG dataset has 2 classes according to the table (and the actual data labels that I checked, which are either 1 or -1), versus in the appendix it says that the dataset has 7 discrete labels. Was wondering if you could clarify the disagreement.

Thank you!

In line 66 of util.py, if tmp > len(row):, I think the intention is to compare tmp against the length of the row just after reading a line of the data file.

However, in line 60, row is updated to be a subarray of length tmp. This is the else clause of tmp == len(row).

This means line 66 will never be satisfied.

For line 52 & 71 in util.load_data(), current node is assumed to be j instead of row[0], isn't this bug here? When reading NCI1.txt, wrong data will be assigned when line 25 reached.

Hello, excellent job！

I have a little question about GIN.
Does GIN make nodes smoother and smoother like GCN?
If not, does node feature represent the structure around this node?

Thanks.

I am wondering whether the discriminative power of GIN can be directly applied to Simple Directed Graphs (no loops, multiple arcs)?