--- tags: Book --- # Sybil Detection Interfaces with Other Groups ![](https://i.imgur.com/Fv2SFKn.png)
Last changed by  
 
bsci-gitcoin
263

Read more

Gitcoin Sybil Detection Book

Summary

8/24/2023
2nd research iteration results & roadmapping

:::info Updated by December 2021 ::: Authors: Danilo Lessa Bernardineli Results under the original scope The definitions & methodology of the research plan were sucessfully implementedThis includes creating rewiring optimizers based on Hill Climbing and Simulated Annealing that were pushed to the NetworkX module <sup><sub>Source: https://github.com/gitcoinco/gitcoin_cadcad_model/tree/main/optimality_gap</sub></sup>

5/23/2023
Sybil Report on Gitcoin Grants Rounds 11

:::info Updated by September 2021 ::: Authors: Danilo Lessa Bernardineli Summary The combined FDD process is effective at catching about 83% of the Sybil Users (between 100% to 57% under 95% CI) according to blind human evaluations. The best estimate for Sybil Incidence on Gitcoin is 6.4%, and is contained between 3.6% to 9.3% with 95% CI. IP clusters together with GitHub account creation date are the most relevant features for detecting sybil users programatically right now. The Fraud Tax is computed as 0.61% of the Funding Amount. Links

5/23/2023
Sybil Report on Gitcoin Grants Rounds 12

Updated by December 2021

5/23/2023
Read more from bsci-gitcoin
Published on HackMD