"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. "

本数据集配套于研究论文《面向动态复杂网络生成的新型框架：融合结构保真度与拓扑表达能力的双图模型相似性驱动划分方法》，为SiCNet（Similarity-driven Complex Network，相似性驱动复杂网络）框架提供了全面的实现资源。SiCNet基于创新的二分划分方法论（MZ方法），该方法最初由Mohammad Zeynali Azim提出，此前曾在IEEE发表的工作中通过元胞自动机（cellular automata）进行建模。本研究将该方法拓展至基于图建模的复杂网络生成领域。本数据集包含SiCNet框架的全部实现文件，其中尤为核心的是用于二分划分图（Division Graph by Two，DGBT）建模的MGraph9864 Python模块，同时涵盖所有脚本、样例输出及对比分析内容。

数据集文件列表与说明（所有文件均包含在压缩归档SiCNet_DataSet中）：
1. MGraph9864.py：实现二分划分图（DGBT）方法论，作为SiCNet中生成DGBT图的核心引擎。内置适用于高级应用的专用函数，例如将DGBT图转换为二值图，以及提取菱形索引与数值（对应函数Dimond_value(dif)与finddimondindex(di)）。
2. Other_models_modularity_community_for_(SiCNet_paper).ipynb：用于评估论文中提及的其他网络模型的模块化、度分布与社区结构的Jupyter笔记本。
3. Other_models_modularity_community_for_(SiCNet_paper).docx：上述代码生成的样例Word输出文件，包含对比结果与可视化内容。
4. SiCNet_Community_Degree_Distribution_CDF.ipynb：用于可视化SiCNet网络结构，检测并展示社区结构，统计各社区的成员数量，并绘制度分布与累积分布函数（Cumulative Distribution Function，CDF）曲线的Jupyter笔记本。
5. SiCNet_Community_Degree_Distribution_CDF.docx：包含自动生成的可视化图表与表格的Word输出文件。
6. SiCNet_Complex_Network_Modularity_Degree_Distribution_CDF.ipynb：用于计算网络模块化系数，在网络图上可视化不同层级的社区结构，并绘制度分布与CDF曲线的Jupyter笔记本。
7. SiCNet_Complex_Network_Modularity_Degree_Distribution_CDF.docx：展示计算结果与网络图的Word输出文件。
8. network_comparison_table_01_22.xlsx：以表格形式全面对比所有参考模型与本文提出的SiCNet框架关键特征的Excel文件。
9. SiCNet_Algorithm.ipynb：包含论文中用于SiCNet网络生成的两种核心算法的仿真实现的Jupyter笔记本。
10. complex_Network_Our_model Copy.enl：包含论文中所有引用文献的EndNote文献库文件。
11. SiCNet_Random_Number_Passed_all_NIST_Tests.png：美国国家标准与技术研究院（National Institute of Standards and Technology，NIST）测试输出截图，证明SiCNet的伪随机数生成能力通过了NIST的所有随机性统计测试。

技术流程：该框架接收两个整数输入(n, m)，生成对应的DGBT图，识别相似的菱形子结构，并将其索引作为所生成复杂网络的边。索引数据以Excel格式系统化存储，可用于密码学、隐写术与伪随机数生成等跨学科应用，同时保留直接构建网络的灵活性。

应用场景：本数据集支持复杂网络建模、去中心化系统仿真、社交网络分析、生物通路建模、基础设施设计，以及量子通信与密码网络等新兴应用领域。完整的代码库可确保SiCNet方法论在多领域内的可复现性与扩展能力。

附加说明：
1. Word输出文件中的所有图像均由本数据集提供的程序自动生成。用户可通过运行对应脚本直接复现这些可视化内容，因为所有网络图与仿真结果均为程序化渲染。
2. 所有文件均打包于名为SiCNet_DataSet的单一压缩归档中，便于获取与下载。
3. 本数据集的所有实现均基于Python语言与Jupyter Notebook环境开发，可被网络科学及相关领域的研究人员与从业者轻松访问与修改。