Topological and temporal stability analysis of the lightning network
Danila Valko and Jorge Marx Gómez
Applied Network Science
The Lightning Network (LN) is the most prominent payment channel network built atop Bitcoin, designed to enable scalable, low-cost off-chain transactions. Understanding its structural evolution and temporal stability is critical for routing optimization, liquidity allocation, and infrastructure robustness. Leveraging a validated dataset of LN topology snapshots spanning 2019–2023, we compute a set of network-science metrics under directed, undirected, unweighted, capacity-weighted, and routing-aware graph representations. To our knowledge, this is the first longitudinal multi-representation temporal stability analysis of the Lightning Network combining topological, distributional, and routing-equivalent persistence metrics over a 5-year validated snapshot dataset. Developed analytical framework reveals a network undergoing gradual structural sparsification and increasing modularization: density, clustering, and global efficiency decline over time, while community fragmentation and centralization persist. However, distributional and observed operational characteristics remain remarkably stable. Degree and shared node capacity distributions exhibit relatively small temporal deviation according to Kolmogorov–Smirnov and Wasserstein diagnostics. The average node retention remains above 90% between successive snapshots. While the average channel retention exceeds 90% in the unweighted representation, it decreases to approximately 70% when evaluated using more realistic pathfinding strategies (LND, CLN, and ECL). Despite this reduction, observed routing-equivalent payment paths remain largely preserved over time. These results indicate that the LN is structurally reconfiguring yet operationally persistent: mesoscopic cohesion decreases, but the routing backbone and economic influence structure remain stable. At the same time, extreme betweenness centralization suggests a growing concentration of routing influence that may increase vulnerability to correlated failures, although explicit robustness under adversarial conditions was not evaluated in this study. Beyond empirical findings, this work provides a comprehensive characterization of a reproducible benchmark dataset and a temporal–topological stability analysis framework for payment channel networks, supporting future research in robustness analysis, routing optimization, and decentralized infrastructure design.
August / 2026
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