LogoLogo
  • OpenRank
    • Ranking and Reputation
    • Use Cases
  • The Reputation Stack
    • Data
    • OpenRank Protocol
    • Apps and Clients
  • Integrations
    • Farcaster
      • Openrank Scores Onchain
      • Ranking Strategies on Farcaster
      • Global Profile Ranking
        • 🔵Top Profiles (based on Following)
        • 🔵Top Profiles (based on Engagement)
        • 🟢Profile Rank (based on Following)
        • 🟢Profile Rank (based on Engagement)
      • Channel User Rankings
        • 🔵Top Profiles in Channel
        • 🟢Profile Rank in Channel
      • Personalized Network
        • Direct Network
          • 🟢Get Direct Following
          • 🟢Get Direct Engagement
        • Extended Network
          • 🟢Personalized Following
          • 🟢Personalized Engagement
      • Frames
        • 🔵Top Frames
        • 🟢Personalized Recommended Frames
      • Feeds
        • For You Feed
          • 🔵For You
          • 🔵For You (by Authorship)
        • Channel Feed
          • 🔵Channel Trending Casts
      • Metadata
        • 🟢Get FIDs for Addresses
        • 🟢Get Handles For Addresses
        • 🟢Get Addresses for FIDs
        • 🟢Get Addresses for Handles
      • Ideas to Build using OpenRank APIs
      • Neynar x OpenRank Guides (WIP)
        • Build "For You" Feeds for your Client, using Neynar and OpenRank
        • Build "User Search" using Neynar and OpenRanks' Global Ranking API
        • Build "Suggested follow list" based on OpenRank and Neynar
        • Build Channel Trending Feeds for your Client using Neynar and OpenRank APIs
        • Build "Discover New Users Feed" using Neynar and OpenRanks Global Ranking API
        • Build Power Badges for your Client using Global & Personalized Ranking APIs by OpenRank
        • Build "Sort Replies" on a cast using Neynar and OpenRanks' Global Ranking API
    • Clanker OpenRank Scores
    • Lens Protocol
      • Ranking Strategies on Lens
      • Lens Profile APIs
      • Lens Content APIs
      • Lens Profile Insights
    • Metamask SPD
    • Onchain Graphs and Feeds
    • Upcoming Integrations
    • GitHub Developers & Repo Ranking
  • Reputation Algorithms
    • EigenTrust
    • Hubs and Authorities
    • Latent Semantic Analysis
  • OpenRank SDK
    • Introduction
    • Creating your first reputation graph
    • Publishing Rankings with OpenRank SDK
    • Guides
      • Tipping based User Rankings powered by OpenRank
    • Installation
    • SDK References
      • EigenTrust
        • Installation and Use
        • Examples for using EigenTrust
      • Hubs & Authorities
        • Installation and Use
        • Examples for using Hubs & Authorities (Coming soon)
      • Latent Semantic Analysis (Coming soon)
Powered by GitBook
LogoLogo

SOCIALS

  • Github
  • Farcaster

Copyright 2024

On this page

The Reputation Stack

PreviousUse CasesNextData

Last updated 1 year ago

The Ranking and Reputation stack

  • Data Layer: This enables any developer or application to bring their own data sets for compute. This involves two main operations - Data sourcing and Pre-processing. Any data source such as onchain attestations, blockchain transactions, open data repositories can participate for sourcing the data. In addition, data analysis and data science communities can bring their own heuristics to form input data sets or reputation graphs as an input for the ranking and reputation compute. The output of the data layer is an [i,j,v] matrix, which is a context-specific reputation graph that will be computed by OpenRank protocol. Read an extensive overview of this in the next page.

  • OpenRank Protocol - A decentralized compute network to ensure verifiable, scalable and permissionless ranking and reputation compute. This layer computes the desirable algorithm (EigenTrust/Hubs and Authorities/Graph neural networks) on the reputation graphs committed by the data layer or clients and provides the converged ranking results (output). The compute nodes provide guarantees of computation and leverage a data availability layer for storing the input and output for any compute operation.

  • Apps and Clients using Rankings - Developers, Protocols and Data scientists can utilize the verifiable rankings data to build desirable reputation, filtration, airdrop, search and discovery, feeds or gating strategies and applications. Rankings of one context can serve as input data for compute for another use case.