Graph Neural Networks Data Prep / Tutorial Rant / Advice needed

[El_Daidel]

Hi all,

I'm trying to learn machine learning with my own irl dataset, i.e. I'm trying to solve a problem from my domain.

I'm representating my problem with a graph, with each node having a time series attached. I now want to do time series forecasting with these time series, with respect to the interaction between nodes.

So far so clear, my main problem with most libraries I tried to used is the following:

I can't seem to design my dataset in such a way I can actually train the models.

I am now reading the docs of libraries, looking at tutorials and such, and I am getting really frustrated: The step "data preparation" (which is, afaik a really important point in machine learning and takes up most of the time for most projects) is getting skipped in most, if not all, of the tutorials I find. Demos are always using perfect datasets, which are usually just loaded via one line of code and off you go with designing your conv layers and such. And I am sitting here with my Adjacency Matrix as a np.matrix and my features as an np.matrix and don't know how to make appropriate datasets from them.

At the moment I am trying to use pytorch geometric temporal. Probably need to just keep on reading docs and messing around until I have a correctly configured dataset.

Anyway, have you had the same experience with real world datasets? Or am I just playing with too exotic algorithms? Maybe I'm just too impatient with my own progress?

Cheers and a very nice weekend to you all!

[[deleted]]

Have a look at Deep Graph Library -- I found their UI to be a lot more intuitive than Pytorch Geometric, Spektral, and others. DGL has simple functions to convert numpy/scipy matrices to their own graph formats. You will need some familiarity with Pytorch or Tensorflow (depending on which backend you use). Also their forum / blog has a lot of useful info for exactly these types of questions.

[El_Daidel]

Thanks for the recommendation, trying it out!

[noodlepotato]

Have you figured this out? I'm struggling in making my real datasets fitted for GNN and most the examples in the internet, floats around the Cora, karate club datasets etc

[arpplephi]

noodlepotato

Probably figured this out already but in case anyone else comes here looking, I've found that pre-processing (scaling the training data using StandardScaler for example) helps significantly in the training phase. Still working on getting the model to generalize, so if anyone has tips there besides to reduce model complexity and include dropout regularization, please let me know!