Viewing a single comment thread. View all comments

laaweel t1_j4pm5q3 wrote

Hello,
it depends on the problem but it is also possible to train over many graphs.
I am also a beginner, especially in the area of graph neural networks, and found it very confusing that in all the examples only one graph was trained on at a time.
But it seems to be no problem. I am currently training a model and have 200k+ example graphs and I do predict node features.
I collected the dataset myself though. But I think there are also datasets with many graphs in the field of biology / medicine.

Feel free to reach out if you need help :)

2

ramya_1995 OP t1_j56ovcx wrote

u/laaweel I have another quick question. Cora dataset splits the labels into 140 trains, 500 for valid and 1000 for test (according to DGL website). I found that these numbers correspond to the number of nodes (node classification problem). But any thought why the sum (140+500+1000) does not match the total node number in Cora dataset (2708 nodes)? Is it because the rest of the nodes are unlabeled? Thank you!

1

laaweel t1_j56s7g8 wrote

I didn't know it either but I found this blog post:

https://medium.com/mlearning-ai/ultimate-guide-to-graph-neural-networks-1-cora-dataset-37338c04fe6f#:~:text=Interesting!%20The%20training%20data%20contains%2020%20data%20for%20each%20class.%20The%20validation%20and%20test%20data%20do%20not%20have%20equal%20proportions%20of%20classes%2C%20but%20the%20two%20have%20a%20similar%20distribution.%20And%20these%20are%20similar%20to%20the%20percentages%20of%20classes%20in%20the%20overall%20data%20seen%20in%20the%20Classes%20section.

2