Papers Relating to Bitcoin and Related Topics in Law: Part III
This article was first published on Dr. Craig Wright’s blogand we republished with permission from the author. Read Part 1, Part 2 and Part 4.
Cimini, G., Squartini, T., Saracco, F., Garlaschelli, D., Gabrielli, A., & Caldarelli, G. (2019). The statistical physics of real networks. Nature reviews Physics, 1(1), Article 1.
The research presented by Cimini et al. documents the statistical physics and algorithmic analysis of network modeling and analysis as it has been developed over the past two decades in connection with complex networks and related phenomena. The main focus of the thesis is associated with a combination of information theory and statistical physics. Such methods can be used to analyze complex real-world networks, including blockchain-based systems. The weakest contribution the authors present is the approach that makes it possible to create null models of complex networks and hence the possibility to analyze and study networks through experimentation. Such null models can help analyze existing networks, and provide frameworks that help explain complex network systems.
The statistical mechanics-based approaches have been presented in a way that extends into the analysis of multilayer networks and can model complex systems. Most critically, the structures and algorithms can be extended to continuous data analysis and dynamic high-dimensional structures. Such models are important when it comes to analyzing a dynamically changing set of nodes in a blockchain network. By defining a system of nodes that can join and leave, and modeling such a system through a changing, dynamic environment, the authors present a methodology for analyzing systems where members dynamically appear and leave.
Liu, X., Li, D., Ma, M., Szymanski, BK, Stanley, HE, & Gao, J. (2022). Network resilience. Physics reports, 9711–108.
The authors have explored and developed the concept of resilience related to computer networks. While the system may also apply to other network-based systems, the focus of the current research project is along the lines of measuring the effect nodes have on a blockchain network, and as such is most relevant when applied to computer networks. The researchers have focused on analyzing resilience functions and explored the use of warning systems to signal potential failure of connected components. Such an approach could lead to the development of early warning systems that can be used in distributed networks and provide a means to detect attacks or increase the stability and robustness of systems connected across such systems.
The authors also provide detailed definitions of terms in network science that are often used ambiguously. By documenting robustness and resilience and describing them in a mathematically rigorous process, the authors have created a terminology and means to measure resilience in multiple systems. The article provides a number of definitions and approaches that will help describe the types of connections between blockchain-based systems. Through such an approach, the use of standardized language and terminology will simplify many of the existing complications found in describing networks and associated treacherous terminology (Walch, 2017).
Shi, Y. (2022). Advances in Big Data Analytics: Theory, Algorithms and Practice. Springer Nature.
This book summarizes and captures many new advances in big data analysis. While it begins by summarizing the development of big data in China and other areas of the academic community, the book quickly expands to more complex algorithmic analysis and the development of new classification engines. The section on classification and optimization describes comprehensive methods involving error correction and linear programming. The main focus is on rule-based methods, but incorporates support vector machines and extends into newer decomposition methodologies and algorithms. The word-based sentiment analysis and link analysis part is interesting, but lacks relevance for current research. Nevertheless, the section on learning analysis and the concept of cognitive learning can be extended to provide automated methods for classifying types of systems, including the analysis of nodes in the dataset proposed in the current study.
The part of the work that is of greatest interest and relevance concerns function analysis and function selection. The section looks at distance-based selection, including domain-driven two-phase methodologies. Such methods can extend the automation of analyzing nodes in a blockchain network. The regularization process developed in the chapter provides opportunities to develop classification schemes to demonstrate the selection of nodes and the relevant impact they have on a network such as a blockchain network. Through this, it would be possible to set discriminatory processes that isolate the relative effect of a node on the network.
Other references
Javarone, MA and Wright, CS (2018). From Bitcoin to Bitcoin Cash: A Network Analysis. Proceedings of the 1st Workshop on Cryptocurrencies and Blockchains for Distributed Systems77–81.
Sampaio Filho, CIN, Moreira, AA, Andrade, RFS, Herrmann, HJ, & Andrade, JS (2015). Mandala Networks: Ultra-small world and very sparse graphs. Scientific reports, 5(1), 9082.
This article has been lightly edited for clarity.
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