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

Dataset File List and Descriptions (all files are included in the compressed archive SiCNet_DataSet):

1. MGraph9864.py
   Implements 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)).
2. Other_models_modularity_community_for_(SiCNet_paper).ipynb
   Jupyter 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).docx
   Sample Word output file generated by the above code, containing comparative results and visualizations.
3. SiCNet_Community_Degree_Distribution_CDF.ipynb
   Visualizes 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.docx
   Word output file with automatically generated visualizations and tables.
4. SiCNet_Complex_Network_Modularity_Degree_Distribution_CDF.ipynb
   Calculates 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.docx
   Word output file showing the results and network diagrams.
5. network_comparison_table_01_22.xlsx
   Excel file presenting a comprehensive comparison of key features of all referenced models and the proposed SiCNet framework in a tabular format.
6. SiCNet_Algorithm.ipynb
   Contains the simulation of the two core algorithms used in the manuscript for SiCNet network generation.
7. complex_Network_Our_model Copy.enl
   The EndNote file containing all references cited in the manuscript.
8. SiCNet_Random_Number_Passed_all_NIST_Tests.png
   A screenshot of the NIST test output, demonstrating that the pseudorandom number generation capability of SiCNet passes all NIST statistical tests for randomness.
9. SiCNet_Generation.ipynb
   This file is dedicated to the construction of the proposed SiCNet complex network. It generates the network edges in two ways: first, by using the NetworkX library to directly construct the SiCNet (G) network; and second, by exporting the edge list to an Excel file for further analysis or visualization. This dual approach allows users to both visualize the network within Python and access the raw edge data externally.

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:

1. 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.
2. All files are organized within a single compressed archive named SiCNet1 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.