Cross-chain generalization capability evaluation of Sybil account detection models based on transaction graph features tests across Ethereum, Polkadot, and Near. Models trained on Ethereum achieve 89% accuracy when deployed on Polkadot but drop to 72% on Near due to graph structure differences. The study proposes domain adaptation techniques combining graph convolutional networks with transfer learning, improving cross-chain performance by 24%.
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Optimization of Subgraph Sampling Strategies for False Detection in Graph Neural Networks During Cross-Chain Message Verification This study optimizes subgraph sampling strategies for false detection in graph neural networks during cross-chain message verification. By improving sampling efficiency and accuracy, we enhance verification reliability, ensuring secure and efficient cross-chain communication in blockchain ecosystems.
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Proof-of-stake (PoS) economics hinges on balancing validator profitability and network security. Low staking rewards discourage participation, weakening decentralization, while high rewards attract rational actors but may inflate costs. Ethereum’s transition to PoS reduced energy use by 99% but requires validators to stake 32 ETH, limiting accessibility. Data indicates that validators earn 4–6% annual returns at current participation levels. However, economies of scale favor large staking pools, risking centralization. Solutions like slashing penalties for malicious behavior and decentralized staking services aim to maintain security while broadening participation. Optimal reward structures must adapt to market conditions to sustain long-term viability.
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