The goal of this issue is to surface reproducible and generalizable ways of obtaining chain-data related to arbitrum that is useful for any kind of Sybil or Delegate network-of-influence analysis.
Examples of interesting data that Pipeline can produce:
- detailed breakdown of gas consumption of grantee contracts
- transactions related to airdrop farming/sybil-activity
- analyzing activity of Arbitrum multisigs
- detecting money tansfers between delegates
Analysts who apply to Sybil hunting bounties or otherwise work with on-chain information are encouraged to package their data-ingestion + data-processing logic into reproducible format that allows to re-apply analysis over fresh data to qualify for this bounty.
For extra credit, pipeline can go beyond reporting on-chain data and apply some models to detect suspicious activity, cluster related wallets together or enrich the data in other creative manner.
We are being open-minded as to definition and construction of a pipeline (docker containers, python package, dagster/airflow DAG), as long as it allows end-users analyst to easily and automatically:
- obtain interesting on-chain data relevant to Arbitrum ecosystem
- apply some interesting analysis over it
- derive insights about Arbitrum on-chain activity
The goal of this issue is to surface reproducible and generalizable ways of obtaining
chain-datarelated to arbitrum that is useful for any kind of Sybil or Delegate network-of-influence analysis.Examples of interesting data that Pipeline can produce:
Analysts who apply to Sybil hunting bounties or otherwise work with on-chain information are encouraged to package their data-ingestion + data-processing logic into reproducible format that allows to re-apply analysis over fresh data to qualify for this bounty.
For extra credit, pipeline can go beyond reporting on-chain data and apply some models to detect suspicious activity, cluster related wallets together or enrich the data in other creative manner.
We are being open-minded as to definition and construction of a pipeline (docker containers, python package, dagster/airflow DAG), as long as it allows end-users analyst to easily and automatically: