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Appendix
Appendix A: Python Code – Creating the Peer Trading Network
Appendix B: Gephi Operations First, the .csv file prepared in Python requires manual adjustment. To upload it as an edge list in Gephi, the columns must be labelled as marked below:
After uploading this file as an edge list (ETH relabeled as weight), the following screen print shows in Gephi shows how we can determine measures for a directed network. The statistical tools of the software give the option to derive network measures for node centrality and degree.
After performing all relevant network measures, export the below spreadsheet to a .csv file and further Python processes.
Appendix C: Python Code – Performance Metrics
Appendix D: Python Code – Merging SNA measures and performance metrics
Appendix E: R-Studio Code –Data Transformation After processing the data in Python, the variables are prepared in R. First, the data needs to be scaled and normalised. As internet topologies follow a power log function, and some values in the set are negative, the Yeo Johnson transformation fits our data best. It becomes evident that the distributions need to be transformed by applying the logarithm. To avoid 0 values, the data is scaled.
Output: bestNormalize(x)
Code: Yeo-Johnson Transformation
Appendix F: R-Studio Code – Multiple Regression – Step(x)
Appendix G: R-Studio Output –Multiple Regression & VIF Summary and VIF of the bids model
Summary and VIF of the ETH model
Summary and VIF of the USD model (not qualified)
Appendix H: Yeo-Johnson – Variables, Correlations, Distributions The following to screen prints show the variables, their correlation, and normal distributions 1) before and 2) after the Yeo-Johnson transformation. The following rows of these tables correspond to the following variables of this analysis.
1. Bidder 2. ETH 3. USD
4. Closeness centrality 5. Betweenness centrality 6. Clustering coefficient 7. In-degree
8. Out-degree 9. Degree
10. Weighted in-degree 11. Weighted out-degree 12. Weighted degree