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