SFFSlib: A Python Library for Optimizing Attribute Layouts from Micro to Macro Scales in Network Visualization *

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.