cv VS CoinBLAS

I'm working on a new version of my CV (v1: https://cv.git-pull.com)

This time I'm writing it in React, Angular, and Vue (TypeScript across the board). It's in the very early stages, but keep it at https://github.com/tony/cv

While doing it, I've tried to find various ways to share the chart and data code across all 3 versions. The library I found to make it happen with data akita: https://datorama.github.io/akita/

For UX it's not so easy. The truth is, some of the best UI libraries are framework specific. This creates a lot of fragmentation in the frontend community IMO. For instance react has nivo, vicious, and react-vis, but at the end of the day it's svg and canvas underneath the hood. Still though, the efforts are amazing. It makes you wonder what it'd be like if there would be just one agreed upon way to write a widget. (There's web components, custom elements... but those won't even work with typescript out of the box)

In the past few years, that stuff that's been going on with webpack v5's persistent caching, TypeScript (and its tooling), and all 3 of these frameworks getting better is amazing.

Examples of charting software I've tried: https://cv-react-v2.git-pull.com/dev/branch/v2-billboard.js/, https://cv-react-v2.git-pull.com/dev/branch/v2-plotly/, https://cv-react-v2.git-pull.com/dev/branch/v2-carboncharts/

For the first time I've found a UI suite called Carbon: https://www.carbondesignsystem.com/, an IBM thing. I'm most impressed by their chart offering. Even though it's relatively new, they maintain bindings for react, vue and angular.

When you consider that a graph and a matrix are isomorphic, doing vector matrix multiplication takes a vector with a set value, say row 4, and multiplies it by a matrix where row 4 has values present that represent edges to the nodes that are adjacent to it (ie "adjacency" matrix). The result is a vector with the next "step" in a BFS across the graph, do that in a loop and you step across the whole graph.

A cool result of this is, for example, taking an adjacency matrix and squaring it is the "Friend of a Friend" graph. It takes every node/row and multiplies it by itself, returning a matrix that are adjacent to the adjacencies of each node, ie, the friends (adjacencies of the adjacencies) of friends (adjacencies) of the nodes.

Deeper traversal are just higher nodes, a matrix cubed are the friends of the friends of the friends.

A picture is worth a thousand words, see figure 7 of this paper:

https://arxiv.org/pdf/1606.05790.pdf

Also check out figure 8, this shows how incidence matrices can work to represent hyper and multi graphs. An pair of incidence matrices reprsent two graphs, one from nodes to edges and the other from edges to nodes, these are n by m and m by n. When you multiply them, you get a square adjacency matrix that "projects" the incidence into an adjacency. This can be used to collapse hypergraphs into simple graphs that use different semirings to combine the multiple edges.

For some pretty pictures of this kind of stuff, check out CoinBLAS (note I am not a crypto-bro, it was just a very handy extremely large multi-graph that I could easily download in chunks to play with):

https://github.com/Graphegon/CoinBLAS/

Python wrapper around The GraphBLAS API:

https://github.com/michelp/pygraphblas

For an upcoming paper we've open sourced using pygraphblas to analyse the bitcoin graph using the GAP benchmarks on a server with 1TB of RAM:

https://github.com/Graphegon/CoinBLAS