Generalization capability evaluation of graph neural network (GNN) models detecting Sybil attacks in decentralized identity systems tests across 5 blockchain networks. Models trained on Ethereum achieve 89% accuracy when deployed on Solana but drop to 72% on Polkadot due to graph structure differences. The study proposes domain adaptation techniques improving cross-chain performance by 24%.
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Correlation Analysis Between Community Structure Evolution and Behavioral Deviation in Governance Token Delegated Voting Networks This study conducts a correlation analysis between community structure evolution and behavioral deviation in governance token delegated voting networks. By tracking network dynamics and voting patterns, we assess how structural changes influence collective decision-making, offering insights for improving governance mechanisms in decentralized organizations.
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Task pricing algorithms in decentralized computing marketplaces dynamically set costs based on supply, demand, and node capabilities. Auction-based models, where nodes bid for tasks, optimize resource allocation but may underprice complex jobs. Predictive algorithms using historical data and real-time metrics (e.g., CPU load) improve accuracy. Experiments show that machine learning-driven pricing reduces task completion times by 20% while maintaining profitability. However, balancing fairness with efficiency remains challenging, as over-optimization could exclude smaller providers. Hybrid models combining auctions with baseline prices offer a viable compromise.
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