The Reputation Stack

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.

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