Graphs(cdixon.org) |
Graphs(cdixon.org) |
And is it just me, or is it painful to read this? It has the feel of a student taking an intro course on optimization, then exclaiming, "everything is an optimization problem!" I mean, sure, this is true, but it's tautological and feels forced to me.
And sorry, but Twitter's main innovation was discarding symmetry? People have been subscribing (an asymmetric relation) to things on the internet and otherwise for ages.
This is a common statement that benefits from explicit context: Twitter's innovation specifically was importing the "asymmetric follow" into a social network, a graph in which nodes represent individual people and in which relations are generally public.
You mean a link? ;)
With regards to your other points, I do think the general premise of the post is correct that the pendulum is swinging back (?) towards more flexible data structures (e.g. graphs).
The writing does come across as though the author just had an epiphany ... hey... these things are all graphs...
Yeah, and so are lots of other things.
Yes, it was painful to read and I missed the whole point of the post unless it is just listing of where we find and use "graphs." I think this is a post out of his comfirt zone; I like his VC and startup posts better.
Apparently Twitter rebranded the concept of a hyperlink.
>"I expect we’ll look back on the next few years as the golden age of graph innovation."
I would say that the 'golden age of graph innovation' began much before this. Which is why in Computer Science, as he describes, we have a special edition of graph theory named 'network theory'. I wonder what that's for!
Graph based thinking is a result of the rise of social networking. The term "social network" wasn't common until I was a senior in college. Back then most computer scientists thought of things in terms of matrices - rows and columns. After 2004 when facebook became the most popular software in the college universe, people became much more interested in graphs (social and otherwise). I believe this lead many young computer scientists to start thinking in terms of graphs - vertices and edges. If you read comp sci papers written by people over the age of 30, many of them still express things in terms of matrices.
In my understanding, graphs can be faster to process and in many cases easier to traverse. But I believe that the shift in thinking has more to do with popular trends in software than it does any technical advantage of graphs over other ways of thinking.
Linear algebra and graph theory do have some deep connections though.