A place for developers to talk about building on Farcaster.

/dev

small mvp of the @balajis.eth follow graph idea

@nobuilder.eth took the following data of the top 20% users from a hub and we plugged it into vis.js

effect is very clustered for now, if you have ideas on how to visualize it please ping us on gh!

play with it here:

Author of The Network State.
Work with me: jobs.ns.com

Amusement, wonder, and uncertainty. now [redacted], prev @neynar ✴︎ libred.org

small mvp of the @balajis.eth follow graph idea

@rafi took the following data of the top 20% users from a hub and we plugged it into vis.js

effect is very clustered for now, if you have ideas on how to visualize it please ping us on gh!

play with it here: https://nobuilder.github.io/farcaster-social-graph/

Great work! Can you try partitioning the graph into dense subgraphs, like below? This would identify the Farcaster tribes.

There are various approximate algorithms for clique detection, dense subgraph identification, and factorization of a matrix into block diagonals that you might use.

There is an inspiring 2006 paper theorizing about defending against Sybil attacks based on social interactions.
Social network analysis will be an important part of building web of trust in pseudonymous networks you often describe.
https://www.math.cmu.edu/~adf/research/SybilGuard.pdf