PageRank mostly involved graph theory in the mere observation that there’s a directed graph of pages linking to each other. It then immediately turns to linear algebra, where the idea is that you want a page’s weight to correspond to the sum of the weights of the pages linking to it—and this exactly describes finding an eigenvector of the graph matrix.
On second thought I guess your idea for karma is more complicated, maybe I’ll look at some simple examples and see what comes up if I happen to have the time.
That’s interesting to know about pagerank. It’s smart it just goes to linear algebra.
I think building the graph requires data that isn’t publicly available like identity of votes and views. It might be hard to get a similar dataset to see if a method works or not. Some of the “clustering techniques” might not apply to other data.
PageRank mostly involved graph theory in the mere observation that there’s a directed graph of pages linking to each other. It then immediately turns to linear algebra, where the idea is that you want a page’s weight to correspond to the sum of the weights of the pages linking to it—and this exactly describes finding an eigenvector of the graph matrix.
On second thought I guess your idea for karma is more complicated, maybe I’ll look at some simple examples and see what comes up if I happen to have the time.
That’s interesting to know about pagerank. It’s smart it just goes to linear algebra.
I think building the graph requires data that isn’t publicly available like identity of votes and views. It might be hard to get a similar dataset to see if a method works or not. Some of the “clustering techniques” might not apply to other data.
Maybe there is a literature for this already.