arXiv subjects

Danila Valko

Publications and source records attributed to Danila Valko.

6 recordsLinked to original sources

arXiv2026

An Empirical Evaluation of BMSSP and Dijkstra's Algorithm for the Lightning Network

Efficient routing is critical for payment channel networks (PCNs) such as the Lightning Network (LN), where shortest-path computations are commonly performed using Dijkstra-based algorithms. Recent theoretical work introduced the Bounded Multi-Source Shortest Path (BMSSP) algorithm, which achieves an asymptotically faster running time than Dijkstra's algorithm on sparse directed graphs. This paper presents an empirical evaluation of BMSSP on real-world LN topology snapshots and compares its performance with a standard Dijkstra implementation written in Rust. Across five LN snapshots and 100 randomized source-node trials per snapshot, BMSSP consistently exhibited higher runtimes than Dijkstra's algorithm, with statistically significant differences observed in all evaluated datasets. These findings indicate that, for current LN graph sizes and the implementations studied, BMSSP does not provide practical runtime advantages despite its stronger asymptotic guarantees. Rather than demonstrating a routing acceleration technique, this work contributes an LN-specific empirical case study of a recently proposed shortest-path algorithm and highlights the gap between theoretical complexity improvements and practical performance on contemporary payment-channel-network topologies.

cs.PF↗

arXiv2026

Topological and Temporal Stability Analysis of the Lightning Network

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.

cs.SI↗

arXiv2026

FAIR+S: A validation study of a framework for sustainable research data and software

The FAIR principles (Findable, Accessible, Interoperable, Reusable) have transformed research data management, but they do not address the environmental impact of creating and using research software and data, such as energy consumption, carbon emissions, and life-cycle impacts that become central to computer science and engineering-related domains. To bridge this gap FAIR+Sustainability or FAIR+S, an extension of the FAIR framework that embeds environmental accountability as a core element, was introduced. Because FAIR principles already structure how digital research artefacts are described, shared, and reused, they offer an effective entry point for embedding sustainability considerations at scale. FAIR+S weaves carbon-footprint and energy-use considerations directly into FAIR-aligned metadata schemas, workflows and development specifications. In doing so, it enables research infrastructures to report, compare, and audit the environmental implications of data and software in a measurable, interoperable, and transparent manner. This creates a foundation for reproducible research that simultaneously advances open science goals and decarbonisation objectives. However, integrating environmental accountability into established research workflows raises questions of feasibility, relevance, and acceptance across stakeholders and disciplines. In this work we validated the framework through a cross-disciplinary expert survey. The evaluation confirms its importance and practical relevance, but also reveals current gaps in researchers' awareness of green software practices.

cs.CY↗

arXiv2026

Shared Channel Capacity and Node Lifetime: An Empirical Study of the Lightning Network

The Lightning Network (LN) is a rapidly evolving payment channel network that enables scalable, off-chain transactions on top of Bitcoin. While prior research has documented its topological structure and liquidity concentration, the joint relationships between node lifetime, connectivity, and capacity remain insufficiently understood. This study provides a comprehensive empirical analysis of these relationships using a large-scale dataset of LN topology snapshots spanning the period 2019-2023. We examine whether node lifetime influences shared channel capacity, and whether this effect is mediated and moderated by node degree. In addition, we account for hierarchical geographic structure and explore the role of country-level economic conditions. The results show that node lifetime has a positive but relatively modest direct effect on capacity. This relationship is largely mediated by node degree, indicating that liquidity accumulation primarily occurs through increased connectivity. Furthermore, the interaction between lifetime and degree reveals significant heterogeneity, with stronger effects observed among highly connected and high-capacity nodes. Mixed-level models demonstrate superior explanatory power, highlighting the importance of country- and region-level variation. The inclusion of GDP per capita confirms that broader economic conditions significantly influence capacity distribution. Overall, the findings suggest that liquidity in the LN emerges from the interplay of temporal dynamics, network structure, and economic context. This study contributes to a more integrated understanding of payment channel networks and provides a foundation for future research on their evolution and efficiency.

cs.NI↗

arXiv2026

mosaiks are made of tesserae: GUI design for a co-simulation framework

In a mosaic, a tessera is a single stone. We introduce tesserae for the co-simulation framework mosaik, where they are sets of entities. They allow for a visual, intuitive, and yet systematic description of simulation scenarios by allowing their entities to be created together and the entities of two tesserae to be connected simultaneously, while ensuring that multidirectional data-flow between tesserae remains consistent without further manual synchronization. We further present an extension of mosaik by a graphical user interface (GUI) based on these tesserae, enabling the drag-and-drop creation of co-simulation setups and their execution. The GUI aims to make mosaik more accessible to users previously excluded by its script-based nature. At the same time, it preserves mosaik's flexibility, extensibility, and modular architecture.

cs.CE↗

arXiv2025

Sustainable broadcasting in Blockchain Networks with Reinforcement Learning

Recent estimates put the carbon footprint of Bitcoin and Ethereum at an average of 64 and 26 million tonnes of CO2 per year, respectively. To address this growing problem, several possible approaches have been proposed in the literature: creating alternative blockchain consensus mechanisms, applying redundancy reduction techniques, utilizing renewable energy sources, and employing energy-efficient devices, etc. In this paper, we follow the second avenue and propose an efficient approach based on reinforcement learning that improves the block broadcasting scheme in blockchain networks. The analysis and experimental results confirmed that the proposed improvement of the block propagation scheme could cleverly handle network dynamics and achieve better results than the default approach. Additionally, our technical integration of the simulator and developed RL environment can be used as a complete solution for further study of new schemes and protocols that use RL or other ML techniques.

cs.NI↗