Continuously crawl the Nostr network (24/7/365), searching for follow lists (kind:3) and other relevant events.
Quickly assess whether new events should be added to the database based on the author's rank. Approved events are used to build a custom Redis-backed graph database.
Generate and maintain random walks for nodes in the graph, updating them as the graph topology evolves.
Use these random walks to efficiently compute acyclic Monte Carlo Pageranks (personalized and global). Algorithms are inspired by this paper

Apps

/cmd/crawl/

The main entry point, which assumes that the event store and Redis are syncronized. In case they are empty, the graph will be initialized using the INIT_PUBKEYS specified in the enviroment.

/cmd/sync/

This mode builds the Redis graph database from the event store. In other words, it syncronizes Redis to reflect the events in the event store, starting from the INIT_PUBKEYS specified in the enviroment, and expanding outward.