A Graph of Related Subreddits

I work for reddit and here I’ve used a similar technique to your jaccard distance but with one twist: divide by the size of the smaller subreddit (in your case, the number of unique posters that you’ve recorded). That gives you a directional relatedness, that is programming->python but not necessarily python->programming. Used this way you account for the giant subreddit problem automatically but now the results are less “amitheasshole is related to askreddit” and more like “linguisticshumor is a more niche version of linguistics”.

The great thing is that it’s actually more actionable as far as recommendations go! Everybody has already heard of the bigger version of this subreddit, but they probably haven’t heard of the smaller versions. And it’s self-correcting. As a subreddit gets bigger we are less likely to recommend it (which is great because it needs our help less)

If you're interested, in the scientific literature this problem is known as "network backboning". Basically you have all nodes connected to practically all other nodes with weighted edges, and you want to know which are the edges with statistically significant weights.

I wrote on this topic [1]. My method [2] basically uses simple counts on edge weights, and then estimates the expected edge weight and its variance using Bayesian priors. It then attaches a t-score or p-value to each edge, and then you can filter out edges with too low t-score.

The idea is that weak edges can still be statistically significant if they connect "small" nodes. In any case, the library I wrote includes the implementation of a few other methods, in case they work better for your data type.

[1] https://arxiv.org/abs/1701.07336 [2] http://www.michelecoscia.com/?page_id=287

A lot of Reddits have "Recommended" or "Related" boxes at the side. Why don't you implement these as first, and then use the algorithm(s)?

This has a similar issue to the way Reddit recommends subs itself. A sub will be considered similar if it's the exact opposite due to users of one sub going to another to shitpost or brigade or etc.

I'm not sure of a fix for that, but would it be possible/helpful to weigh it by average upvote/downvote of the comments left from users of said sub? Meaning if sub A is about how much baseball sucks, and sub B is about how amazing baseball is, while determining if the 2 are similar you'd find out most posts from sub A to sub B are heavily downvoted and so probably not similar.

>I used my own metric that is based on jaccard similarity. Which in turn is based on "users who posted to X also posted to Y" metric.

Is that really a good metric of similarity? Just myself, I post in several unrelated subreddits semi-regularly from programming to video games to music, art and even stone masonary, i've posted in subreddits for TV shows i've watched, or just completely random things.

I use reddit as a place where I can learn about and interact with people on nearly any subject or topic and I take advantage of that when I can. I'm sure my posting habits aren't that unusual. I'm just not sure that's really an accurate way to gauge similarity.

Probably due to both subreddits going on trolling raids on each other.

It depends on what you are looking for. Tensors as in physics (typically with each dimension being related to space(time)), tensors for quantum information & computing, tensors for deep learning?

I've found it illuminating to see tensor diagrams.

- https://medium.com/@pmigdal/in-the-topic-of-diagrams-i-did-w...

- https://www.math3ma.com/blog/matrices-as-tensor-network-diag...

Also, right now, we are developing a matrix visualization in https://github.com/Quantum-Game. However, it is pretty much work in progress; for a slightly more mature one, go to Quantum Game 2 website and in the element encyclopedia there is one.

I am always up for talking about tensors, so feel invited to drop me an email.

I found r/chairsunderwater while looking for a subreddit that could help me pick out a new office chair.

r/chairs has 800 people subscribed. r/chairsunderwater has 115k. It's just reddit things ¯\_(ツ)_/¯

Makes sense, JP's reddit fanbase is quite small. One slice of his videos has been seen across all the edits and platforms 500million times. He has insane reach.