A Guide to Data Science for the Lightning Network
- You will gain exposure to areas of application for data science in the Lightning Network
- You will have access to a set of code and gossip data; you can run the code to see the application of these concepts
- You will interact with mempool and Blockstream APIs to explore the data they expose. You will have code and sample output from LND API.
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How Many Nodes and Channels: Covers the basics of the Lightning Network, focusing on counting and analyzing the number of nodes and channels in the network.
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Basic Stats: Introduces fundamental statistical analysis of the Lightning Network, covering key metrics and basic data interpretation.
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Network Graph: Explores how to create and interpret network graphs of the Lightning Network, visualizing connections between nodes.
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Graph Metrics: Delves deeper into specific metrics used to analyze network graphs, such as centrality measures and clustering coefficients.
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Pathfinding: Covers algorithms and techniques for finding optimal payment routes through the Lightning Network.
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Probing: Discusses methods for probing the Lightning Network to gather information about channel capacities and network topology.
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LND API Calls: Focuses on interacting with Lightning Network Daemon (LND) nodes through API calls, demonstrating how to programmatically access network data.
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More Visualization: Explores advanced visualization techniques for Lightning Network data, building on the foundations of earlier chapters.
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HTLC Analysis: Covers the analysis of Hash Time Locked Contracts (HTLCs), which are fundamental to Lightning Network operations, including patterns in HTLC usage and their implications for network behavior.
Each chapter is represented by a Jupyter notebook, providing a hands-on, code-based approach to learning about Lightning Network analytics.