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Ke-Chao Zhang(张可超), Sheng-Yue Jiang(蒋升跃), and Jing Xiao(肖婧). 2025: SFFSlib: A Python library for optimizing attribute layouts from micro to macro scales in network visualization, Chinese Physics B, 34(5): 058903. doi: 10.1088/1674-1056/adcb98
Citation: Ke-Chao Zhang(张可超), Sheng-Yue Jiang(蒋升跃), and Jing Xiao(肖婧). 2025: SFFSlib: A Python library for optimizing attribute layouts from micro to macro scales in network visualization, Chinese Physics B, 34(5): 058903. doi: 10.1088/1674-1056/adcb98
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SFFSlib: A Python library for optimizing attribute layouts from micro to macro scales in network visualization

  • 1 College of Big Data and Internet, Shenzhen Technology University, Shenzhen 518118, China;

  • 2 School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, China;

  • 3 College of Information and Communication Engineering, Dalian Minzu University, Dalian 116600, China

  • Received Date: 24/12/2024
    Accepted Date: 20/03/2025
  • Fund Project:

    Project supported by the National Natural Science Foundation of China (Grant Nos. 61773091 and 62476045), the LiaoNing Revitalization Talents Program (Grant No. XLYC1807106), and the Program for the Outstanding Innovative Teams of Higher Learning Institutions of Liaoning (Grant No. LR2016070).

  • PACS: 89.75.Fb; 87.23.Ge; 05.10.-a

  • Complex network modeling characterizes system relationships and structures, while network visualization enables intuitive analysis and interpretation of these patterns. However, existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales, particularly failing to provide advanced visual representations of specific nodes and edges, community affiliation attribution, and global scalability. These limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual representation. To address these limitations, we propose SFFSlib, a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature visualization. Notably, we have enhanced the visualization of pivotal details at different scales across diverse network scenarios. The visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout algorithms. The experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales, offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.
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SFFSlib: A Python library for optimizing attribute layouts from micro to macro scales in network visualization

  • Received Date: 24/12/2024
  • Accepted Date: 20/03/2025
Fund Project: 

Abstract: Complex network modeling characterizes system relationships and structures, while network visualization enables intuitive analysis and interpretation of these patterns. However, existing network visualization tools exhibit significant limitations in representing attributes of complex networks at various scales, particularly failing to provide advanced visual representations of specific nodes and edges, community affiliation attribution, and global scalability. These limitations substantially impede the intuitive analysis and interpretation of complex network patterns through visual representation. To address these limitations, we propose SFFSlib, a multi-scale network visualization framework incorporating novel methods to highlight attribute representation in diverse network scenarios and optimize structural feature visualization. Notably, we have enhanced the visualization of pivotal details at different scales across diverse network scenarios. The visualization algorithms proposed within SFFSlib were applied to real-world datasets and benchmarked against conventional layout algorithms. The experimental results reveal that SFFSlib significantly enhances the clarity of visualizations across different scales, offering a practical solution for the advancement of network attribute representation and the overall enhancement of visualization quality.

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