A Novel Framework for Dynamic Complex Network Generation: A Similarity-Driven Division by Two Graph Model Integrating Structural

This dataset accompanies the research paper \u201cA Novel Framework for Dynamic Complex Network Generation: A Similarity-Driven Division by Two Graph Model Integrating Structural Fidelity and Topological Expressiveness\u201d and provides comprehensive implementation resources for the SiCNet (Similarity-driven Complex Network) framework.SiCNet is based on an innovative division-by-two methodology (MZ-method), originally developed by Mohammad Zeynali Azim and previously modeled through cellular automata in IEEE-published work. In this study, the approach is extended to complex network generation using graph-based modeling. The dataset includes all implementation files for the SiCNet framework, notably the MGraph9864 Python module for Division Graph by Two (DGBT) modeling, as well as all scripts, sample outputs, and comparative analyses.Dataset File List and Descriptions (all files are included in the compressed archive SiCNet_DataSet):MGraph9864.pyImplements the Division Graph by Two (DGBT) methodology, serving as the core engine for generating DGBT graphs in SiCNet. Includes specialized functions for advanced applications, such as converting DGBT graphs to binary graphs and extracting diamond indices and values (see functions Dimond_value(dif) and finddimondindex(di)).Other_models_modularity_community_for_(SiCNet_paper).ipynbJupyter Notebook for evaluating other network models discussed in the manuscript with respect to modularity, degree distribution, and community structure.Other_models_modularity_community_for_(SiCNet_paper).docxSample Word output file generated by the above code, containing comparative results and visualizations.SiCNet_Community_Degree_Distribution_CDF.ipynbVisualizes the SiCNet network structure, detects and displays communities, tabulates the number of members in each community, and plots the degree distribution and Cumulative Distribution Function (CDF).SiCNet_Community_Degree_Distribution_CDF.docxWord output file with automatically generated visualizations and tables.SiCNet_Complex_Network_Modularity_Degree_Distribution_CDF.ipynbCalculates network modularity, visualizes communities with different ranks on the network graph, and plots the degree distribution alongside the CDF.SiCNet_Complex_Network_Modularity_Degree_Distribution_CDF.docxWord output file showing the results and network diagrams.network_comparison_table_01_22.xlsxExcel file presenting a comprehensive comparison of key features of all referenced models and the proposed SiCNet framework in a tabular format.SiCNet_Algorithm.ipynbContains the simulation of the two core algorithms used in the manuscript for SiCNet network generation.complex_Network_Our_model Copy.enlThe EndNote file containing all references cited in the manuscript.SiCNet_Random_Number_Passed_all_NIST_Tests.pngA screenshot of the NIST test output, demonstrating that the pseudorandom number generation capability of SiCNet passes all NIST statistical tests for randomness.Technical Workflow:The framework accepts two integer inputs (n, m), generates corresponding DGBT graphs, identifies similar diamond substructures, and uses their indices as edges in the resulting complex network. Index data is systematically stored in Excel format to enable interdisciplinary applications including cryptography, steganography, and pseudorandom number generation, while maintaining flexibility for direct network construction.Applications:This dataset supports research in complex network modeling, decentralized system simulation, social network analysis, biological pathway modeling, infrastructure design, and emerging applications in quantum communication and cryptographic networks. The comprehensive code base enables reproducibility and extension of the SiCNet methodology across multiple domains.Additional Notes:All images included in the Word output files are automatically generated by the programs provided in this dataset. Users can reproduce these visualizations directly by running the corresponding scripts, as all network diagrams and simulation results are rendered programmatically.All files are organized within a single compressed archive named SiCNet_DataSet for convenient access and download.All implementations are developed in Python using Jupyter Notebook environment, ensuring accessibility and ease of modification for researchers and practitioners in network science and related fields.