Distributed Token Allocation via Network Centrality Sampling
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Solution Overview
Problem
Decentralized computing platforms face challenges in efficiently computing measures of network centrality, particularly with algorithms like Shapley values, which scale poorly and become infeasible as the number of nodes increases, affecting computations in decentralized applications such as content distribution platforms.
Innovation Solution
A process involving a distributed computer system to determine network centrality scores and allocate cryptographic tokens based on these scores, using a mint-and-burn blockchain-based feedback-communication protocol to manage token allocations, ensuring efficient computation and fair contributor rewards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional algorithms like Shapley values are used to compute network centrality measures, then measurement precision is improved, but computing performance deteriorates due to exponential scaling O(2^n) with the number of nodes
Solution Approach 1:
The patent segments the computation of network centrality measures by introducing sampling mechanisms that divide the full node set into smaller subsets. Instead of computing Shapley values for all nodes, the system samples representative subsets and computes centrality measures on these smaller sets, reducing computational complexity from exponential to manageable levels while preserving measurement accuracy through proper sampling methodology.
Solution Approach 2:
The patent changes the computational parameters by transitioning from exact Shapley value computation to approximate methods with controlled error margins. By adjusting sampling size, confidence levels, and precision parameters, the system achieves a practical balance between measurement precision and computing performance, enabling scalability to large decentralized networks.
2Adaptability or versatility
If decentralized computing platforms use permissionless access allowing un-trusted third parties, then adaptability is improved, but security and reliability worsen due to Byzantine general problems
Solution Approach 1:
The patent implements feedback mechanisms where nodes continuously verify and report the behavior of other nodes. Through reputation systems and consensus validation, the network receives feedback about node reliability and adjusts trust levels dynamically. This feedback loop enables the system to maintain high adaptability while mitigating Byzantine problems by identifying and isolating malicious behavior.
Solution Approach 2:
The patent introduces intermediary mechanisms such as smart contracts and consensus protocols that mediate interactions between un-trusted third parties. These intermediaries enforce agreement rules, validate transactions, and coordinate node behavior without requiring central authority, thereby maintaining platform accessibility while ensuring reliability through cryptographic verification and decentralized consensus.
Data Source
AI summary
Described processes include: determining portions of instances of a cryptographic token to be allocated to record providers, like providers of an asset indicated by a record, wherein: the portions are determined based on network effects associated with the records the record provider supplied on performance of a computer-implemented network in which both record providers and record consumers participate, patterns indicative of inorganic consumption may be determined from one or more of interactions of individual consumers, interactions of collections of consumers, or consumer interactions in the aggregate for a given provider or record; and the effects on network performance are adjusted responsive to designation of one or more entities as exhibiting inauthentic behavior; and appending to a distributed ledger, records indicating the respective portions, and adjustments, are allocated to record providers.