利用结构力量实现供需平衡的支付通道网络

中文摘要 AI 辅助

区块链技术面临可扩展性挑战，因为交易必须在全网范围内验证和记录。支付通道网络（PCNs）通过将交易移至链下、仅在主链上记录关键交互来提升效率。然而，PCNs需要预存的通道余额（供给）与交易需求（需求）相匹配，余额不足会导致供给短缺。现有方法包括交易路径优化和通道余额分配，虽能解决该问题，但因需动态适应波动的需求而产生高成本。我们通过揭示流动性分配的结构组织，发现PCNs中的余额缺口与网络拓扑之间存在关联，这使得无需额外补充余额即可从静态拓扑视角通过通道重构缓解供需冲突。我们进一步提出支付拓扑熵（PTE），这是一种信息论度量，用于量化每个节点与全局平均连接模式的偏差并捕捉余额供给的结构特性。基于PTE，我们设计了MaxPTE拓扑优化算法，通过结构重构重新组织各通道间的余额分配，使静态余额分布与动态交易需求相匹配。大量实验表明，MaxPTE可将余额缺口降低27.25%，平均最大流提升12.23%，交易失败概率降低25.96%，性能优于现有基准方法。该方法在不同的余额-需求分布下仍保持稳健，无需预先预测需求即可改善供需平衡。

英文摘要

Blockchain technology faces scalability challenges because transactions must be validated and recorded across the network. Payment channel networks (PCNs) improve efficiency by moving transactions off-chain and recording only critical interactions on the mainnet. However, PCNs require pre-deposited channel balances (supply) to match transaction demands (demand), and insufficient balances cause supply shortages. Existing approaches, including transaction path optimization and channel balance allocation, address this problem but incur high costs due to dynamic adaptation to fluctuating demands. We reveal a correlation between balance deficits in PCNs and network topology by uncovering the structural organization of liquidity allocation. This enables supply-demand conflicts to be mitigated from a static topological perspective through channel reconfiguration, without additional balance replenishment. We further introduce payment topological entropy (PTE), an information-theoretic metric that quantifies each node's deviation from the global average connection pattern and captures structural properties of balance supply. Based on PTE, we design MaxPTE, a topology optimization algorithm that reorganizes balance allocation across channels through structural reconfiguration, aligning static balance distribution with dynamic transaction demand. Extensive experiments show that MaxPTE reduces balance deficits by 27.25%, increases average maximum flow by 12.23%, and decreases transaction failure probability by 25.96%, outperforming existing benchmarks. The method also remains robust across diverse balance-demand distributions, improving supply-demand balance without prior demand prediction.

发表机构

• College of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）
• Beijing Institute of Technology, Zhuhai（北京理工大学珠海校区）

机构由 AI 辅助整理，请以论文原文为准。