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This header file has lots of commentary.
https://github.com/google/graph-mining/blob/main/in_memory/c...
This, too:
https://github.com/google/graph-mining/blob/main/in_memory/s...
Same with most of the other files.
How is it usable? It's usable if you want to find date within lots and lots of data efficiently. That's kinda Google's thing. :-D
Really though an open source product has not really been released until there is documentation walking through setting it up and doing some simple thing with it. As it is I am really not so sure what it is, what kind of hardware it can run on, etc. Do you really think it got 117 Github stars from people who were qualified to evaluate it?
(I’d consider myself qualified to evaluate it.. If I put two weeks into futzing with it.)
Every open source release I’ve done that’s been successful has involved me spending almost as much time in documentation, packaging and fit-and-finish work as I did getting working it well enough for me. It’s why I dread the thought of an open source YOShInOn as much as I get asked for it.
Sometimes though it is just a bitch. I have some image curation projects and was thinking of setting up some “booru” software and found there wasn’t much out there that was easy to install because there are so many moving parts and figured I’d go for the motherf4r of them all because at least the docker compose here is finite
https://github.com/danbooru/danbooru
even if it means downloading 345TB of images over my DSL connection.
For those wanting to play with graphs and ML I was browsing the arangodb docs recently and I saw that it includes integrations to various graph libraries and machine learning frameworks [1]. I also saw a few jupyter notebooks dealing with machine learning from graphs [2].
Integrations include:
* NetworkX -- https://networkx.org/
* DeepGraphLibrary -- https://www.dgl.ai/
* cuGraph (Rapids.ai Graph) -- https://docs.rapids.ai/api/cugraph/stable/
* PyG (PyTorch Geometric) -- https://pytorch-geometric.readthedocs.io/en/latest/
--
1: https://docs.arangodb.com/3.11/data-science/adapters/
2: https://github.com/arangodb/interactive_tutorials#machine-le...
For those wanting to play with graphs and ML I was browsing the arangodb docs recently and I saw that it includes integrations to various graph libraries and machine learning frameworks [1]. I also saw a few jupyter notebooks dealing with machine learning from graphs [2].
Integrations include:
* NetworkX -- https://networkx.org/
* DeepGraphLibrary -- https://www.dgl.ai/
* cuGraph (Rapids.ai Graph) -- https://docs.rapids.ai/api/cugraph/stable/
* PyG (PyTorch Geometric) -- https://pytorch-geometric.readthedocs.io/en/latest/
--
1: https://docs.arangodb.com/3.11/data-science/adapters/
2: https://github.com/arangodb/interactive_tutorials#machine-le...