PdfPig VS NetworkX

Maybe (most likely) this is a problem of GitHub's terminology. For genuine bugs, e.g. here's the repro, the stack trace, the code to replicate it, it happens 100% of the time if you follow these steps, I'd agree that just having it open and in the backlog would be preferable.

The problem is those make up maybe at a generous estimate, 10-15% of issues in a projects backlog. In the interests of full disclosure here's mine (I don't use stalebot) https://github.com/UglyToad/PdfPig/issues?page=1&q=is%3Aissu.... As you can see from the backlog I close almost nothing. This was a deliberate choice to avoid closing things until the fix was confirmed by the reporter.

But equally that's the first time I've opened the repository in a couple of months and the amount of angst and dread I feel just from the size of that list means I'll probably find yet another excuse not to do anything on it this coming month.

Discussions on this topic feel a lot like "technical solutions to social problems"; by which I mean "well in the ideal world a perfectly logical person would do x, y, z so the system should reflect that". And while a stalebot is the archetypal technical solution to a social problem it at least works with how maintainers work. Sometimes in life you want to ignore a problem and have it go away. When you can't do that, e.g. government bureaucracy, work stuff, social obligations, that's where stress comes from. And asking volunteer maintainers to add a whole new source of stress in their life falls apart when people get busy, or their life circumstances change, or they get ill or tired or whatever.

Yes, in a perfect world the issue backlog would be sacrosanct and perfectly groomed/prioritized. But we're just fleshy sacks of chemicals and we're not perfect. Unrealistic expectations from users are the cause of maintainer burnout.

Because GitHub closed issues are still viewable and searchable (I'd guess most people search it through a search engine not the terrible inbuilt search) I'd disagree that they're deceiving users somehow.

What about PdfPig? It's under Apache 2.0.

You could try PdfPig. https://uglytoad.github.io/PdfPig/ I've used it for some small tasks and found it very useful. If you want to handle scanned pdfs you would need to use OCR instead.

PDF Pig is open source and allows you to read text and even extract images.

https://uglytoad.github.io/PdfPig/ https://github.com/empira/PDFsharp

Example of document creation API here https://github.com/UglyToad/PdfPig#document-creation-005 and wiki with more details here https://github.com/UglyToad/PdfPig/wiki/Document-Creation

Example with PDFpig https://github.com/UglyToad/PdfPig/blob/master/examples/GeneratePdfA2AFile.cs

Built from this data... https://github.com/networkx/networkx/blob/main/examples/grap...

I think one of the elements that author is missing here is that graphs are sparse matrices, and thus can be expressed with Linear Algebra. They mention adjacency matrices, but not sparse adjacency matrices, or incidence matrices (which can express muti and hypergraphs).

Linear Algebra is how almost all academic graph theory is expressed, and large chunks of machine learning and AI research are expressed in this language as well. There was recent thread here about PageRank and how it's really an eigenvector problem over a matrix, and the reality is, all graphs are matrices, they're typically sparse ones.

One question you might ask is, why would I do this? Why not just write my graph algorithms as a function that traverses nodes and edges? And one of the big answers is, parallelism. How are you going to do it? Fork a thread at each edge? Use a thread pool? What if you want to do it on CUDA too? Now you have many problems. How do you know how to efficiently schedule work? By treating graph traversal as a matrix multiplication, you just say Ax = b, and let the library figure it out on the specific hardware you want to target.

Here for example is a recent question on the NetworkX repo for how to find the boundary of a triangular mesh, it's one single line of GraphBLAS if you consider the graph as a matrix:

https://github.com/networkx/networkx/discussions/7326

This brings a very powerful language to the table, Linear Algebra. A language spoken by every scientist, engineer, mathematician and researcher on the planet. By treating graphs like matrices graph algorithms become expressible as mathematical formulas. For example, neural networks are graphs of adjacent layers, and the operation used to traverse from layer to layer is matrix multiplication. This generalizes to all matrices.

There is a lot of very new and powerful research and development going on around sparse graphs with linear algebra in the GraphBLAS API standard, and it's best reference implementation, SuiteSparse:GraphBLAS:

https://github.com/DrTimothyAldenDavis/GraphBLAS

SuiteSparse provides a highly optimized, parallel and CPU/GPU supported sparse Matrix Multiplication. This is relevant because traversing graph edges IS matrix multiplication when you realize that graphs are matrices.

Recently NetworkX has grown the ability to have different "graph engine" backends, and one of the first to be developed uses the python-graphblas library that binds to SuiteSparse. I'm not a directly contributor to that particular work but as I understand it there has been great results.

In the project we used Python lib networkx and a DiGraph object (Direct Graph). To detect a table reference in a Query, we use sqlglot, a SQL parser (among other things) that works well with Bigquery.

If you program in Python, can use NetworkX for that. But it's probably a good idea to implement the basic algorithms yourself at least one time.

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/

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1: https://docs.arangodb.com/3.11/data-science/adapters/

2: https://github.com/arangodb/interactive_tutorials#machine-le...

org-roam-ui is a great interactive visualization tool, but its main use is visualization. The hope of this library is that it could be part of a larger graph analysis pipeline. The demo provides an example graph visualization, but what you choose to do with the resulting graph certainly isn't limited to that. See for example networkx.