Documents Associated with Bitcoin and Related Topics in Law: Part XI

[ad_1]

This article first appeared on Dr. Craig Wrights blog, and we have republished with permission from the author. Read part 1, part 2, part 3, part 4, part 5, part 6, part 7, part 8, part 9 and part 10.

Measuring the distance between nodes has been considered complex (Chen et al., 2020) and subject to error or listed computer processing. Due to network measurement issues, some authors have been content to capture and produce representations of a network of graphs, without analyzing the power impact and influence of each node. A limited approach to this form has been conducted on the Ethereum network, providing a model of computer systems on the network (Kim et al., 2018); yet such an approach fails to distinguish the validity of each node or its influence on other parts of the network.

Fei (2018) addresses some network influence analysis issues left unaddressed by other authors, such as Kim et al. (2018). The new approach for identifying influential nodes in complex networks also summarizes other existing centrality measures and provides a way to capture the intensity and mutual attraction that may exist between nodes in a distributed network such as Bitcoin. Through such a process, the author provides a way to compare the influence of each node, allowing researchers to analyze the power or comparative effect that each system maintains.

Through such analysis, Fei (2018) provides a way to isolate core systems and network factors that form giant node components within complex networks, while Chen et al. (2020) discuss automated machine learning technologies that can simplify some of the tasks. Unfortunately, many authors, including Kim et al. (2018), continue to focus on node volume, ignoring the individual effect that the most influential nodes have on the rest of the network.

Annotated Bibliography

Chen, D., Lin, Y., Li, W., Li, P., Zhou, J. & Sun, X. (2020). Measure and relieve over-smoothing problem for graph neural networks from topology view.

Chen et al. (2020) introduce the concept of graphical neural networks (GNNs) as a machine learning model connected to graphical representation. The methodology incorporates mean absolute deviation (MAD) and smoothing characteristics from many other neural networks and related machine learning tools. The feature also enables network data capture through an automated process with a set of tools known as the Adaptive Edge Optimization System. Finally, the topography measurements are analyzed for noise and over-smoothing.

The main benefit of the paper lies in the systematic and quantitative analysis of the problems encountered by GNNs and the optimization of systems that analyze and capture graph topographies. The analysis is performed on various Pubmed and related public citation networks, and emphasis has been placed on pruning and capturing important information between systems that are not naturally grasped and represented as such.

Fei, L., Zhang, Q. and Deng, Y. (2018). Identification of influential nodes in complex networks based on the inverse square law.

Fei et al. (2018) document the process associated with identifying influential nodes in complex networks. By determining the most influential and indispensable nodes for the transmission and dissemination of traffic, an analysis of decentralization and interconnectivity between systems will be possible. The article begins by addressing the different types of centrality measures that already exist, documenting the shortcomings and limitations of the algorithm. Next, the authors propose to use an inverse square law to form an index of mutual attraction between nodes in a complex network.

The paper presents a series of experiments and simulations comparing the proposed centrality measure to existing measures such as closeness centrality, degree centrality and eigenvector centrality. In addition, the methods used with web systems such as Google (NASDAQ: GOOGL) in PageRank and LeaderRank are also analyzed. Finally, the model and process extends to the examination of epidemiological systems, including susceptible and infected models. Experimental validation demonstrates good statistical power in the proposed methodology.

Kim, SK, Ma, Z., Murali, S., Mason, J., Miller, A. & Bailey, M. (2018). Measure Ethereum network peers.

Kim et al. (2018) propose a methodology to measure network peers in the Ethereum network. The document begins by introducing and documenting Ethereum’s smart contract capability, and refers to the system as a cryptocurrency. Next, the argument is presented that Ethereum is Turing’s first complete blockchain system, ignoring the capabilities of Bitcoin. The method is based on node discovery and is deployed using a developed tool called NodeFinder. The authors claim that the tool found over 10,000 nodes while crawling the Ethereums P2P ecosystem.

No analysis of node functionality, such as block development, is provided, and measurement and validation focuses on research of all system participants. The argument is made that the tool is validated by external measurements where the node finds other systems that are part of the peer ecosystem. Unfortunately, no information regarding block creation has been released. Similarly, no information regarding the dissemination or transmission of blocks or transactions is included in the study. Therefore, non-productive peer analysis (2018, p. 99) brings little benefit. Additionally, since the authors did not distinguish between network client nodes and nodes that actively produce blocks and transmit information, the overall value of the article is limited.

Additional references Chen, D., Lin, Y., Li, W., Li, P., Zhou, J., & Sun, X. (2020). Measure and relieve over-smoothing problem for graphical neural networks from topology view. ., Zhang, Q., & Deng, Y. (2018). Identify influential nodes in complex networks based on the inverse square law.Physica A: Statistical Mechanics and Its Applications,512, 10441059. https://doi.org/10.1016/j.physa.2018.08.135Kim, SK, Ma , Z., S. Murali, J. Mason, A. Miller and M. Bailey (2018). Measure Ethereum network peers.

This article has been slightly edited for clarity.

Watch: Dr. Craig Wrights Keynote: A Better Internet with IPv6 and BSV Blockchain

New to Bitcoin? Check out CoinGeek’s Bitcoin for Beginners section, the ultimate resource guide to learn more about the Bitcoinas originally envisioned by Satoshi Nakamoto and blockchain.

[ad_2]