Structural Entropy Based Visualization of Social Networks

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dc.contributor.authorXue, Mingliangen_US
dc.contributor.authorChen, Luen_US
dc.contributor.authorWei, Chunyuen_US
dc.contributor.authorHou, Shuoweien_US
dc.contributor.authorCui, Lizhenen_US
dc.contributor.authorDeussen, Oliveren_US
dc.contributor.authorWang, Yunhaien_US
dc.contributor.editorChristie, Marcen_US
dc.contributor.editorHan, Ping-Hsuanen_US
dc.contributor.editorLin, Shih-Syunen_US
dc.contributor.editorPietroni, Nicoen_US
dc.contributor.editorSchneider, Teseoen_US
dc.contributor.editorTsai, Hsin-Rueyen_US
dc.contributor.editorWang, Yu-Shuenen_US
dc.contributor.editorZhang, Eugeneen_US
dc.date.accessioned2025-10-07T06:05:02Z
dc.date.available2025-10-07T06:05:02Z
dc.date.issued2025
dc.description.abstractSocial 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.en_US
dc.description.sectionheadersVisualization
dc.description.seriesinformationPacific Graphics Conference Papers, Posters, and Demos
dc.identifier.doi10.2312/pg.20251302
dc.identifier.isbn978-3-03868-295-0
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.2312/pg.20251302
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/pg20251302
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Visualization → Information Visualization; Network Visualization; Graph Layout
dc.subjectVisualization → Information Visualization
dc.subjectNetwork Visualization
dc.subjectGraph Layout
dc.titleStructural Entropy Based Visualization of Social Networksen_US
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