· 2 min read

SimClusters

SimClusters means we’re all living in the influencer dimension now https://dl.acm.org/doi/10.1145/3394486.3403370

This image contains a paragraph of text discussing the advantages of converting a directed graph into a bipartite graph, particularly in social networks like Twitter, where it's beneficial to make the right set (R) smaller than the left set (L) to efficiently represent most edges.

Basically, Twitter uses a latent space of simulated “communities” as the topology of the network. The top 1% of users are considered “influencers.” If a group of influencers have a lot of shared incoming connections, they become a “community.”

These simulated clusters go on a gigantic sparse matrix with all types of content: users, tweets, hashtags, “Events.” This way they can use nearest-neighbor analysis rather than heavy matrix multiplications, as well as representing all content in the same latent space.

All users can belong to one or more of these communities, and so can all tweets. Influencers can change the shape of these communities through their actions, but only indirectly: attracting or repelling different followers. If you’re not an influencer, you’re basically a tweet.

Oh, that’s right in the linked post, lol.

This is very reminiscent of Twitterverse graphs, I think.

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