Layout results generated by six methods for the eight social networks in Facebook100 dataset [RA15]. Nodes with higher degrees tend to be more inclined towards the color red.
Material
Abstract
Social networks exhibit the small-world phenomenon, characterized by highly interconnected nodes (clusters) with short average path distances. While force-directed layouts are widely employed to visualize such networks, they often result in visual clutter, obscuring community structures due to high node connectivity. In this paper, we present a novel approach that leverages structural entropy and coding trees to enhance community visualization in social networks. Our method computes the structural entropy of graph partitions to construct coding trees that guide hierarchical partitioning with O(E) time complexity. These partitions are then used to assign edge weights that influence attractive forces in the layout, promoting clearer community separation while preserving local cohesion. We evaluate our approach through both quantitative and qualitative comparisons with state-of-the-art community-aware layout algorithms and present two case studies that highlight its practical utility in the analysis of real-world social networks. The results demonstrate that our method enhances community visibility without compromising layout performance. Code and demonstrations are available at https://github.com/IDEAS-Laboratory/SEL.