snorkel VS nodevectors

The paid product came out of an open source tool: https://github.com/snorkel-team/snorkel

Actually, I referenced it in my issue as well. There seems to be different utils.py file in different folders under the snorkel-tutorials repo but the utils file we get after importing snorkel has a different [file](https://github.com/snorkel-team/snorkel/blob/master/snorkel/utils/core.py) ,i.e. the utils file is different in the main snorkel repo

Snorkel (https://github.com/snorkel-team/snorkel) might provide you exactly what you are looking for. From the docs:

Yes, people working on graph based ML realize quickly that the underlying data structures most originally academic libraries (networkX, PyG, etc.) use are bad.

I wrote about this before [1] and based a node embedding library around the concept [2].

The NetworkX style graphs are laid out as a bunch of items in a heap with pointers to each other. That works at extreme scales, because everything is on a cluster's RAM and you don't mind paying the latency costs of fetch operations. But it makes little sense for graphs with < 5B nodes to be honest.

Laying out the graph as a CSR sparse matrix makes way more sense because of data locality. At larger scales, you could just leave the CSR array data on NVMe drives, and you'd still operate at 500mb/s random query throughput with hand coded access, ~150mb/s with mmap. That remains to be implemented by someone.

[1] https://www.singlelunch.com/2019/08/01/700x-faster-node2vec-...

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

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.

You could also use some node embedding library to embed the sparse matrix into a denser one and then cluster that.

Numba fits very few usecases, but where it does fit it's awesome.

I've been using it in a python graph library to write graph traversal routines and it's done me very well: https://github.com/VHRanger/nodevectors

The best part is the native openMP support on for loops IMO. Makes parallelism in data work very efficient compared to python alternatives that use processes (instead of threads)

I was playing around with graph embeddings (https://github.com/VHRanger/nodevectors/) and wanted to visualize them, which led me to look into UMAP.

The best candidates for it would be UMAP or graph embedding methods

As expected, networkx couldn't handle more than a million nodes so I had to search for python libs which might handle that much data.

This is why I've been using your lib (https://github.com/VHRanger/nodevectors) for at least 2 weeks now as well as these 2 other libs: https://github.com/louisabraham/fastnode2vec and https://github.com/sknetwork-team/scikit-network. What do they have in common? They handle sparse graphs (using CSR representations).

Having a graph with several million nodes isn't just some edge case, social graph for instance grow way faster than anyone could expect.