On-Chain Reputation Ranking by Adaptive Weighted PageRank

Abstract

Reputation is a fundamental concept in human social interactions and inter-relationship. Blockchain and Web3 have been rapidly growing, involving multi-millions of users, billions of transactions, and hundreds of millions of addresses onchain (i.e. on-blockchain). Decentralized identity and onchain reputation will play a critical role in defining each individual's persona, and natively supporting onchain entities to build trust in the Web3 space, while preserving user privacy and autonomy. This paper presents a novel reputation ranking method to address the problem. We leverage adaptive weighted Page Rank algorithms to assess and rank the reputation of participants within various public blockchains. We assign reputation scores to onchain entities based on their historical transaction behaviors, taking transaction volume, activeness, and network contributions into account. In addition to describing the theoretical framework of our reputation ranking, this paper provides empirical insights into its implementation on transactional datasets of Ethereum and BNB Chain. The results and findings presented herein offer a foundation for further research and development of onchain reputation systems in public blockchains and its applications in decentralized finance and Web3 ecosystems.