通过基于三角形的内聚子图简化社会网络

IF 3.8 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Rusheng Pan , Yunhai Wang , Jiashun Sun , Hongbo Liu , Ying Zhao , Jiazhi Xia , Wei Chen
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引用次数: 0

摘要

简化大型社交网络的节点链接图的一个主要挑战在于,简化的图通常包含密集的子组或内聚子图。图三角形量化了维持内聚子图的坚实和稳定的关系。了解内聚子图中三角形的机制有助于阐明社会网络中的连接模式。然而,以往的研究很难处理和可视化内聚子图中的三角形。在本文中,我们提出了一种基于三角形的图简化方法,该方法可以通过利用称为k-truss的三角形连接和力定向算法来过滤和可视化内聚子图。我们设计并实现了triggraph,一个基于网络的可视化界面,为探索和分析社交网络提供详细的信息。与现有方法的定量比较,对现实世界数据集的两个案例研究以及领域专家的反馈证明了triggraph的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Simplifying social networks via triangle-based cohesive subgraphs

One main challenge for simplifying node-link diagrams of large-scale social networks lies in that simplified graphs generally contain dense subgroups or cohesive subgraphs. Graph triangles quantify the solid and stable relationships that maintain cohesive subgraphs. Understanding the mechanism of triangles within cohesive subgraphs contributes to illuminating patterns of connections within social networks. However, prior works can hardly handle and visualize triangles in cohesive subgraphs. In this paper, we propose a triangle-based graph simplification approach that can filter and visualize cohesive subgraphs by leveraging a triangle-connectivity called k-truss and a force-directed algorithm. We design and implement TriGraph, a web-based visual interface that provides detailed information for exploring and analyzing social networks. Quantitative comparisons with existing methods, two case studies on real-world datasets, and feedback from domain experts demonstrate the effectiveness of TriGraph.

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来源期刊
Visual Informatics
Visual Informatics Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.70
自引率
3.30%
发文量
33
审稿时长
79 days
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