A Survey on the Visual Analytics of Multivariate Networks

As a common data structure,multivariate network does not only describe complex topological relationships among nodes with edges,but also record various attributes associated with nodes.A large number of visual analytics methods have been proposed enabling users to get deeper insights into the complex features and association patterns of multivariate networks.In this paper,we give a survey on the visual analytics of multivariate networks.Firstly,we introduce a variety of presentations for multivariate networks,such as coordinated views,graph abstraction,attribute-driven layout,on-node/edge encoding and graph matrix.Then,a set of interactions designed for the exploration of multivariate networks are classified into three categories,including the view level,visual-structure level and data level.Furthermore,we investigate the applications of visual analytics of multivariate networks in a variety of domains including social network,deep learning,etc.Finally,the challenges and future work of multivariate network visualizations are discussed and concluded.