nodevectors

Right, RCM assumes a symmetric matrix. Doing something like a single pass of 1d embedding algos like GGVec [1] (note: I wrote the algo, but it's effectively just the GLoVe algo for general graphs) or ProNE (if you have enough RAM) are also very cheap computationally [1] https://github.com/VHRanger/nodevectors

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. Here's the dream (remains to be implemented by someone): Laying out the graph as a CSR sparse matrix makes way more sense because of data locality. You have an array for edges per node, an index pointer array, and then one matrix with a row per edge for edge data, and one matrix with a row per node

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. It's simply too noisy. 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/gr

Network node embeddings are the best for tabular data. I maintain a library on it here, but there's plenty of good alternatives: https://github.com/VHRanger/nodevectors

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 need to handle (for work) a graph with 40 million nodes and more than 130 million edges. 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. So I do totally agree that there is a gap between popular graph libs which handle very small graphs and real life libs which need to handle way bigger graphs. Btw, thank