Onchain Reputation

Data Science
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Reputation is a fundamental concept that has been central to human social interactions and interrelationship. It refers to the collective, transactions, beliefs or opinions held by others about a person or entity, and it can have a significant impact on trust, credibility, and success, especially in the digital world. With the rapid growth of the Web3 space, which involves multi-millions of users, billions of transactions, and hundreds of millions of wallet addresses on various blockchains, in a decentralized manner, measuring the specific reputation of an entity has become increasingly important.

Coinbase calls for onchain Reputation

Recently, Coinbase calls onchain Reputation builders for its Base Ecosystem Fund. The Base’s request for builders claims that “decentralized identity and reputation will play a critical role in defining each individual’s onchain persona, and reputation protocols (like Google Pagerank type) should natively support onchain entities to build trust onchain, while preserving user privacy and autonomy.”

Octan Solution

Inspired by Google PageRank, Octan Network has researched onchain reputation problem since 2019 with two scientific research papers published on globally-recognized academic journals. In 2022, we invented Octan Reputation Ranking System, a powerful engine that uses advanced mathematical ranking algorithms (e.g. adaptive Pagerank, Hodge Rank) to calculate the reputation scores of users and other entities from onchain transactions within various Web3 ecosystems. Reputation scores can be carried by Octan soulbound token, a kind of Web3 ID, to prove user trustworthiness across multiple chains.

Together with reputation ranking, Octan also labels and classifies onchain entities to make onchain data more readable and understandable. Then, Octan Analytics Platform aggregates reputation scores, labels, identification & classification to extract valuable social insights and user persona in Web3 space, providing investment firms, venture capitals, media agencies, communities, a universal metrics and quantitative methods to evaluate dapps, to segment users.

Octan’s Applications

Octan solution is similar to Google Analytics but distinguished from Chainalysis, Nansen, Dune Analytics, Arkham and other onchain analytics firms. Reputation scores and Octan analytics solutions has direct applications in Web3 marketing problems (e.g. user qualification & segmentation, preventing bot/clone account, whitelisting for airdrop/retro/IDO campaigns, etc) and dapp evaluation (measuring a dapp’s importance & impact within a blockchain ecosystem, its user quality by reputation scores).

We highly appreciate any feedbacks and comments.

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I’m not normally a reactionary, but one thing that has always concerned me about online reputation systems in general and immutable ones in particular is the fact that inherent in the idea of a reputation is the fact that half of the people will be on the good side and the other half will be on the bad side. (Unless you’re in Lake_Wobegon where everyone is above average).

Another thing that reputation systems have inherent to them is a feedback loop, so if you’re on the wrong side, your opportunities to right yourself become increasingly more difficult if for no other reason than there are less opportunities (because what else is a reputation system for other than to grant increasingly more important opportunities to people with better reputations).

Is this a concern of your work? If so, can you link to some discussion about these concerns? If not, should it be? How does one “right oneself” if one makes a mistake for example? How does one correct incorrect information that negatively effects one’s reputation? Is there a statute of limitations? If yes, how exactly does that work on an immutable store?

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first, we don’t think good-bad rate is 50-50 always, possibly varies due to different contexts. In the case of onchain transactions, we think goodness is more than a haft (but dunno exactly how much). This is the main approach we are focusing now.
Regarding feedback loop, it is a big concern for a reviewing/rating system, we are investigating it. Correcting oneself is also interesting as your friends or open community can give a help. One deserves all rights and wrongs he/her does.

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Impressed by Octan Network’s innovative reputation approach in Web3. Practical applications have clear potential, especially for Web3 marketing and dapp evaluation. Privacy and scalability should be priorities, and user adoption requires building trust. Looking forward to seeing this concept evolve!

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