OpenRank Protocol

The key objective of the OpenRank protocol is to accept a reputation graph dataset that contains a list of peers and their pairwise trust, and produce a list of scores for all the peers in the dataset. The properties ensured by OpenRank are:

  • Anyone can post transactions for computing ranking, reputation or recommendation

  • The user transactions containing the reputation graph are available on a DA network

  • The computed scores and rankings are posted to a DA network for permissionless usage

  • The computed scores and ranking are verifiable

A detailed explanation of the protocol is covered in the litepaper.

Ranking and Reputation Compute

The compute nodes receive application or context-specific reputation graphs from the data providers and run a graph algorithm computation until we get a converged eigenvector. This eigenvector, along with input data and its blueprint is committed to the data availability layer. Any developer, smart contract or protocol can trustlessly access the resulting scores or ranking and use it in their own use case.

Developers can choose the type of algorithm they want to use. Additionally, they can run any number of different ranking compute jobs by changing various model parameters in the computation - set of seed peers, confidence level of seed peers, weights and biases, global vs. personalized ranking compute. For a detailed understanding of all the parameters in EigenTrust and other algos that can be expressed as GNNs, refer to the litepaper.

After the Compute Node has finished running a specific job, they are responsible for making a commitment of resulting values, since these values will need to be verified by other network participants.

Live Use Cases powered by OpenRank Compute

The live use cases powered by OpenRank compute include:

  1. EigenTrust on Farcaster and Lens graph data to generate global and personalized rankings and recommendations for users, frames, channels and feeds.

  2. Hubs and Authorities on NFT transaction data on Ethereum to generate a NFT ranking system that is based on transitive purchase transactions, which helps in weeding out reputable NFTs from spam/scam.

  3. EigenTrust on EVM transaction data such as p2p token transfers to power a personalized onchain ranking and recommendation for any EOA.

  4. EigenTrust on peer-to-peer token tip transactions to power rankings for Airdrops.

  5. Matrix Factorization on NFT ownership and EOA-to-contract interaction transactions to power a personalized recommendation of reputable onchain artifacts and power a trending/valuable users and contracts lists for any chain.

  6. EigenTrust on Metamask Snaps Directory experimental attestation data to generate a community sentiment for finding safe and trustworthy snaps based on peer-to-peer attestations from reputable software security experts and auditors.

Last updated


Copyright 2024