deodel VS grape

You could use this new classifier deodel that is very robust. It deals seamlessly with missing data, nulls, mixed numerical and categorical attributes, and multi-class targets. You can see an application with this tool:

Just to mention that there is also a new algorithm that is immune to the imbalance of data. An implementation in python is available at: - https://github.com/c4pub/deodel

The deodel classifier can act as a quick dataset evaluation tool. If your data is available in table format, you can check its potential for prediction/classification. Just feed it to deodel. It accepts mixed attributes without any preliminary curation. It simply considers attribute values expressed as floats (dot decimal) as being continuous. It accepts even a mix of continuous and categorical values for the same attribute column.

Ideally, you'd embed the graph into 2 or 3d first, then visualize it as a scatterplot.

Visualizing the edges at scale doesnt yield nice results in general.

The way to do it is to reduce the graph to some 300d or 500d embeddings, then use TSNE/UMAP/PACMAP to reduce that to 3d. Then visualize.

My prefered way is to use some first order embedding method like GGVec in this library [1] (disclaimer I wrote it). Node2Vec and ProNE don't yield great embeddings for visualization (the first is too filamented, the second too close to the unit ball).

Another great library to do this work is GRAPE [2]. Try first-order embedding methods, or short walks on second order methods to avoid the embeddings being too filamented by long random walk sampling.

[1] https://github.com/VHRanger/nodevectors

[2] https://github.com/AnacletoLAB/grape/

For graph embeddings, there's quite a few. I'd recommend this one, but there's also this one (disclaimer: I'm the author) or this one, more of a DGL library.