面向虚拟支付通道的、基于结构感知搜索的可扩展精确路径选择方法
Scalable Exact Path Selection via Structure-Aware Search for Virtual Payment Channels
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中文总结 AI 辅助
针对虚拟支付通道路径选择的高计算成本问题，提出基于四叉树搜索的结构感知精确求解器，结合复合指标实现灵活权衡，在12552节点的真实拓扑上获2-5个数量级加速，延迟低于100毫秒。
中文摘要 AI 辅助
虚拟支付通道（VPC）支持支付通道网络（PCN）中的高效链下交易，但其性能取决于选择高质量的底层路径。现有方法要么依赖简化的指标，要么产生高计算成本。我们研究广义单调指标下的VPC路径选择问题，提出一种基于四叉树搜索的结构感知精确求解器。通过利用单调性和距离平台特性，该方法在保持最优性的同时，剪枝了容量受限搜索空间的大量区域，大幅减少了最短路径计算的次数。我们进一步用整合经济成本和安全风险的复合指标实例化该框架，使其能在不同应用场景间灵活权衡。在合成图和真实闪电网络拓扑（最多12552个节点）上的实验表明，与现有工作相比，该方法实现了2至5个数量级的加速，且延迟始终低于100毫秒。
英文摘要
Virtual Payment Channels (VPCs) enable efficient off-chain transactions in Payment Channel Networks (PCNs), but their performance depends on selecting high-quality underlying paths. Existing approaches either rely on simplified metrics or incur high computational cost. We study VPC path selection under generalized monotone metrics and propose a structure-aware exact solver based on quadtree search. By exploiting monotonicity and distance plateau properties, our method prunes large regions of the capacity-constrained search space while preserving optimality, significantly reducing the number of shortest-path computations. We further instantiate the framework with a composite metric that integrates economic cost and security risk, enabling flexible trade-offs across application scenarios. Experiments on synthetic graphs and real-world Lightning Network topologies (up to 12,552 nodes) show 2--5 orders of magnitude speedup over prior work, with consistent sub-100ms latency.
