Understanding the Bitcoin Ecosystem: A Graph-Based Exploration of BIPs

Bitcoin Improvement Proposals (BIPs) are essential to the evolution of the Bitcoin protocol, characterized by both their individual attributes (e.g., status, categories) and interrelationships (e.g., dependencies, succession). This project aims to mine and structure BIP data, archiving it in a browsable format that captures both these characteristics and connections. Through graph-based visualizations and analysis, we seek to enable a more interactive exploration of the BIP landscape, enhancing both understanding and insight into the proposals and their roles within the ecosystem.

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Context

Bitcoin is a decentralized, peer-to-peer electronic currency, continuously evolving through contributions from its open-source community. At the heart of this development are Bitcoin Improvement Proposals (BIPs), which define the requirements and features that developers follow when implementing protocol changes. BIPs guide the ongoing evolution of Bitcoin and serve to clearly identify and communicate proposed features within the ecosystem.

Graph databases, on the other hand, provide a powerful method for representing non-relational data, making it possible to explore both the individual characteristics and relationships between BIPs using advanced analytic tools and visualizations. This project aims to combine BIPs and graph databases, offering new insights into the influence, structure, and interconnectedness of these proposals.

Motivation

While BIPs follow a structured format, their current textual representation is limited in terms of interactive capabilities. The static nature of browsing BIPs can obscure insights into their relationships and influence across the Bitcoin landscape. By mining and organizing BIPs into a more dynamic format, we can facilitate a richer, more interactive browsing experience. Additionally, through the use of graph analysis, it becomes possible to detect important features such as highly connected BIPs (key proposals), subgraphs, or clusters of related BIPs, providing a more holistic understanding of their influence and evolution within the Bitcoin ecosystem.

Goal

Building on previous work, the goal is now to extend and refine the existing BIP archiving, mining, and visualization framework. The focus will be on improving the mining process, enriching the archived data with more information, and implementing more sophisticated graph-based analysis and visualizations, allowing for better and more immersive BIP exploration. Achieving these objectives requires progress in the following work packages:

  1. Extended Mining and Archiving: The first WP focuses on enhancing extraction techniques by integrating natural language processing (NLP) tools for topic detection, summarization, and entity recognition. To enrich contextual information, the dataset will be expanded with external sources such as forum discussions, GitHub issues, or Google trends. Ideally, this extended context should encompass prominent Bitcoin-related software implementations (e.g., wallets, full nodes) to capture how these applications claim to adhere to BIP specifications. All captured data should be archived in a structured manner to accurately track the evolution of BIPs over time and support comprehensive version history analysis.

  2. Graph-Based Anysis: The second WP involves implementing anomaly detection techniques to identify inconsistencies or unusual patterns in the BIP landscape. For a start, one can apply clustering techniques to categorize BIPs into thematic (feature-related) groups and utilized temporal graph analysis to observe trends, dependencies, and the evolution of BIPs over time. Building on the contextually mined data from WP1, these insights can be extended through cross-analysis with external sources to assess the broader impact of BIPs across different communities and time periods.

  3. Interactive Vizualization: The final WP focuses on developing an interactive graph visualization tool for dynamically exploring BIP relationships. This may include techniques such as force-directed graphs, hierarchical trees, or heat maps. Ideally, it will feature pre-arranged views that highlight key insights, such as non-compliant BIPs, categorizations based on well-known feature names, or timeline-based visualizations of BIP progression and acceptance rates. A dashboard can also provide key metrics and insights, offering an overview of both the derived data analysis and the ongoing mining process.

Requirements

Required skills:

Recommended skills (to be acquired while working on the topic):

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