计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (7): 363-371.doi: 10.11896/jsjkx.251200041

• 计算机网络 • 上一篇    下一篇

面向自治系统关系演化模式理解的可视分析方法

蒋鹏, 汤井威, 陈佳辉, 潘晓杰, 夏新宇, 刘健, 王云超, 孙国道, 梁荣华   

  1. 浙江工业大学计算机科学与技术学院 杭州 310023
  • 收稿日期:2025-12-08 修回日期:2026-03-23 出版日期:2026-07-15 发布日期:2026-07-10
  • 通讯作者: 孙国道(guodao@zjut.edu.cn)
  • 作者简介:(jiangpeng@zjut.edu.cn)
  • 基金资助:
    国家重点研发计划(2022YFB3104800);国家自然科学基金(62422607,62372411);浙江省自然科学基金(LR23F020003)

Visual Analysis Method for Understanding Evolution Patterns of Autonomous System Relationships

JIANG Peng, TANG Jingwei, CHEN Jiahui, PAN Xiaojie, XIA Xinyu, LIU Jian, WANG Yunchao, SUN Guodao, LIANG Ronghua   

  1. College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
  • Received:2025-12-08 Revised:2026-03-23 Published:2026-07-15 Online:2026-07-10
  • About author:JIANG Peng,born in 2001,postgra-duate.His main research interests include data mining and graph visualization analysis.
    SUN Guodao,born in 1988,professor,Ph.D supervisor,is a member of CCF(No.74309S).His main research in-terests include urban visualization,vi-sual analytics of social media,and information visualization.
  • Supported by:
    National Key Research and Development Program of China(2022YFB3104800), National Natural Science Foundation of China(62422607,62372411) and Zhejiang Provincial Natural Science Foundation(LR23F020003).

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摘要： 自治系统路由关系具有高度动态性,受多种因素影响而持续变化,导致现有分析手段在刻画其结构演化和识别关键变化等方面仍存在效率低和表达能力不足的问题。为此,提出一种面向自治系统拓扑演化的交互式可视分析方法,以支持对网络结构变化的识别与理解。首先,基于自治系统商业关系构建带权网络模型,并通过社区检测提取不同时间片的结构聚集模式及其规模、影响力和连接类型等核心特征。随后,结合结构特征与关系变动,采用基于社区的事件分类方法,将业务关系的变化抽象为社区的合并、分裂、扩张与收缩等结构事件,实现拓扑演化的统一表达。最后,通过事件矩阵和径向视图完成对社区时空演化及其层级关系的关联分析。两个案例研究和用户评估结果,验证了该方法在辅助用户分析网络拓扑变化上的有效性和实用性。

关键词: 可视分析, 可视化, 自治系统, 拓扑分析, 网络可视化

Abstract: The routing relationships of autonomous systems(ASs) exhibit high dynamics,constantly changing due to multiple influencing factors,which leads to the inefficiency and insufficient expressiveness of existing analytical methods in characterizing their structural evolution and identifying key changes.To address this,this paper proposes an interactive visual analysis approach for autonomous system topology evolution,aiming at supporting the identification and understanding of network structure changes.Firstly,a weighted network model is constructed based on AS business relationships,and core features such as structural clustering patterns,scale,influence,and connection types across different time slices are extracted through community detection.Subsequently,combining structural features with relational changes,a community-based event classification method is employed to abstract the changes in business relationships into structural events,enabling a unified representation of topology evolution.Finally,event matrices and radial views are used to perform associative analysis of community spatiotemporal evolution and their hierarchical relationships.Two case studies and user evaluation results validate the effectiveness and practicality of the proposed approach in assisting users in analyzing network topology changes.

Key words: Visual analysis, Visualization, Autonomous system, Topological analysis, Network visualization

中图分类号: 

  • TP391
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