piral VS NetworkX

Piral Framework - Focused on developer experience with features like a visual UI editor, live previews and hot module replacement. Ref - https://piral.io/

Micro-frontend applications have become increasingly popular among developers thanks to their many advantages. It helps create modular and maintainable applications capable of handling complex business needs. As with any technology, implementing micro-frontends poses challenges, such as ensuring consistent APIs. But, with tools like Piral, developers can easily create and scale micro-frontend applications.

Piral: implements isolated components called pilets. Pilets are modules that bundle content and behavior.

I've prepared a demo project on the basis of Piral. The running demo can be found at the Piral samples organization on GitHub. Running the demo locally does not look very spectacular.

https://bit.dev/ https://piral.io/ https://github.com/umijs/qiankun https://github.com/single-spa/single-spa

3) Piral: Piral is a framework for next-gen portal applications.

For the command line tooling of our micro frontend framework Piral we needed to be sure that it properly runs. This includes

The idea of writing a book about micro frontends was born in mid 2019 when Piral was born out of smapiot's open-source efforts. We've been leading and assisting to micro frontend implementations for a while, and our intention was to put together an (almost) ideal pattern into an open-source framework.

Therefore, for the example, I've picked a rather fancy way to "loosely" get the micro frontends at runtime using a file called feed.json, which is created at build-time using the information which micro frontends (called pilets in this case, because I am using the Piral framework) are actually available. Therefore, just adding, e.g., a third micro frontend easily works without touching the app-shell package.

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.