Structural Entropy Based Visualization of Social Networks
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
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.
Description
CCS Concepts: Visualization → Information Visualization; Network Visualization; Graph Layout
@inproceedings{10.2312:pg.20251302,
booktitle = {Pacific Graphics Conference Papers, Posters, and Demos},
editor = {Christie, Marc and Han, Ping-Hsuan and Lin, Shih-Syun and Pietroni, Nico and Schneider, Teseo and Tsai, Hsin-Ruey and Wang, Yu-Shuen and Zhang, Eugene},
title = {{Structural Entropy Based Visualization of Social Networks}},
author = {Xue, Mingliang and Chen, Lu and Wei, Chunyu and Hou, Shuowei and Cui, Lizhen and Deussen, Oliver and Wang, Yunhai},
year = {2025},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-295-0},
DOI = {10.2312/pg.20251302}
}