RankLens: Visual analysis of evolving rankings in multiple network

Explaining node rankings in complex networks facilitates deeper analysis of network information. However, incorporating network evolution into influence analysis introduces many new challenges. In this paper, we propose a set of metrics for evaluating the influence of nodes on ranking evolution, enabling the identification of suspicious entities. Building on this, we analyze these entities from both behavioral and topological perspectives to derive insights into network evolution, forming a unified framework for explaining ranking evolution. We further develop RankLens , a visual analytics system designed to observe node ranking evolution and capture the underlying causes of those changes. A case study demonstrates the usefulness and effectiveness of our system in facilitating visual analysis of ranking evolution and assisting experts in identifying the key actors responsible for those changes.