Visual Analytics of Multiple Network Ranking Based on Structural Similarity

Abstract

Ranking the node importance in complex networks has been widely applied for different purposes, such as web search, resource allocation, and network security. However, existing node ranking methods are almost single network ranking using only one relationship, or aggregate the node ranking scores on multiple networks with equal weight, which are insufficient to construct reasonable multiple network rankings, since the association information among multiple networks is largely ignored. Thus, we propose a multiple network visualization framework by fusing multiple networks to obtain credible node ranking scores. After measuring the scores of nodes in each single network by the classic PageRank, a network weight self-adjustment model based on structural similarities between pair-wise networks is designed to strengthen the common features of multiple networks or their distinct characteristics. Then, a combined score for each node is computed by a weighted sum of its individual ranking scores on multiple networks. Besides, we provide a set of visualization and interaction interfaces, enabling users to intuitively explore, optimize and compare the multiple network rankings. Case studies on real datasets show that our system is flexible to adapt to different application scenarios, and users can successfully solve multiple network ranking tasks efficiently.