This article was first published on Dr. Craig Wright’s blog, and 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-world networks. Nature Reviews Physics, 1(1), Article 1. https://doi.org/10.1038/s42254-018-0002-6
The research presented by Cimini et al. documents the statistical physics and algorithmic analysis of network modelling and analysis as it has been developed, in the past two decades, in association with complex networks and related phenomena. The primary focus of the paper is associated with a combination of information theory and statistical physics. Such methods can be applied to analyze complex real-world networks, including blockchain-based systems. The weakest contribution the authors present is the approach that allows for creating null models of complex networks and hence the ability to analyze and study networks through experimentation. Such null models can aid in analyzing 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 extend into continuous data analysis and dynamical 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 with nodes that can join and leave, and modelling 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, B. K., Stanley, H. E., & Gao, J. (2022). Network resilience. Physics Reports, 971, 1–108. https://doi.org/10.1016/j.physrep.2022.04.002
The authors have explored and developed the concept of resilience related to computer networks. While the system can also apply to other network-based systems, the focus of the current research project is aligned with measuring the effect nodes have on a blockchain network and, as such, most relevant when applied to computer networks. The researchers have focused on analyzing resilience functions and explored the use of alert systems to signal potential failure to connected components. Such an approach can lead to developing early warning systems that may be applied 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 paper provides a series of definitions and approaches that will aid in describing the types of connectivity between blockchain-based systems. Through such an approach, the use of standardized language and terminology will simplify many of the existing complications that exist in describing networks and associated treacherous terminology (Walch, 2017).
Shi, Y. (2022). Advances in Big Data Analytics: Theory, Algorithms and Practices. Springer Nature.
This book summarizes and captures many new advances in big data analytics. While it begins by summarizing developments of big data within China, and other areas of the academic community, the book quickly expands into more complex algorithmic analysis and the development of new classification engines. The section on classification and optimization details comprehensive methodologies involving error correction and linear programming. The primary 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 section are interesting, but lack relevance to current research. Yet, the section on learning analysis and the concept of cognitive learning could be extended to provide automated methods to classify types of systems, including the analysis of nodes in the data set proposed in the current study.
The section of the work that is of most interest and relevance concerns the functional analysis and feature selection. The section looks at distance-based selection, including domain-driven two-phase methodologies. Such methodologies could 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 upon 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, M. A., & Wright, C. S. (2018). From Bitcoin to Bitcoin Cash: A network analysis. Proceedings of the 1st Workshop on Cryptocurrencies and Blockchains for Distributed Systems, 77–81. https://doi.org/10.1145/3211933.3211947
Sampaio Filho, C. I. N., Moreira, A. A., Andrade, R. F. S., Herrmann, H. J., & Andrade, J. S. (2015). Mandala Networks: Ultra-small-world and highly sparse graphs. Scientific Reports, 5(1), 9082. https://doi.org/10.1038/srep09082
This article was lightly edited for clarity purposes.
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