A graph neural network-based model for identifying risk contagion paths in DeFi protocols analyzes interconnected smart contract dependencies. Node embeddings capture protocol features, while edge weights represent transaction flows. The model predicts cascading failure probabilities under stress scenarios, enabling proactive risk mitigation. It enhances systemic risk assessment in complex DeFi ecosystems.
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跨链桥流动性池压力测试中，如何模拟多链协同压力？ Simulating multi-chain collaborative stress in cross-chain bridge liquidity pools involves creating synthetic asset pairs across testnets (e.g., Ethereum, Solana). Use agent-based modeling to mimic arbitrageurs exploiting price differentials, triggering rebalancing. Introduce latency variations and network partitions to test resilience. Implement flash loan attacks to assess smart contract vulnerabilities under extreme conditions. Monitor slippage and liquidity crunch thresholds using real-time analytics. Compare results against historical exploits (e.g., Wormhole’s $320M hack) to validate risk models.
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