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Ranking algorithms in traditional search engines are powered by enormoustraining data sets that are meticulously engineered and curated by acentralized entity. Decentralized peer-to-peer (p2p) networks such astorrenting applications and Web3 protocols deliberately eschew centralizeddatabases and computational architectures when designing services and features.As such, robust search-and-rank algorithms designed for such domains must beengineered specifically for decentralized networks, and must be lightweightenough to operate on consumer-grade personal devices such as a smartphone orlaptop computer. We introduce G-Rank, an unsupervised ranking algorithmdesigned exclusively for decentralized networks. We demonstrate that accurate,relevant ranking results can be achieved in fully decentralized networkswithout any centralized data aggregation, feature engineering, or modeltraining. Furthermore, we show that such results are obtainable with minimaldata preprocessing and computational overhead, and can still return highlyrelevant results even when a user's device is disconnected from the network.G-Rank is highly modular in design, is not limited to categorical data, and canbe implemented in a variety of domains with minimal modification. The resultsherein show that unsupervised ranking models designed for decentralized p2pnetworks are not only viable, but worthy of further research.

G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P Network