Visualization Of State Evolution Of Dynamic Complex Networks

Most of the networks in real world are dynamic networks with time-varying attributes.Applying visualization techniques to dynamic complex networks can assist users in analyzing network data,discovering data features,exploring the evolution of the network in a better way.On the basis of analyzing main problems of the existing dynamic complex networks visualization technologies,research in this thesis mainly focuses on the visualization methods of changes of the state and the nodes relation in the visualization technologies of state evolution of dynamic complex networks.The main work includes the following aspects.Firstly,aiming at the problem that the existing visualization methods of state changes of dynamic complex networks cannot express the state cluster and evolution clearly,a method that introduces eigenvector similarity to visualization of state evolution of dynamic networks is proposed.To avoid the data loss caused by excessive dimension reduction,and keep the original characteristics of the time steps,this method reduces the data to its intrinsic dimension to get the eigenvector of the data.The eigenvector similarity is calculated and introduced to the force-directed layout algorithm.By adding the similarity-force and gravity,the layout of state clusters and evolution are much clearer.Experiments show that the proposed visualization method can present states and the trajectory of dynamic network which changes over time more clearly and directly.Secondly,aiming at the problem that the existing node ranking algorithms for dynamic complex networks cannot evaluate the nodes importance objectively because of the over consideration of influence of time and the comparison graph cannot present abundant networks information,an improved node ranking algorithm and a two layers of concentric circles visualization method are proposed.In order to evaluate nodes importance more objectively in dynamic networks,the PageRank algorithm is firstly improved by introducing eigenvector similarity.Then,based on the comparison graph,the two layers of concentric circles visualization method is proposed to present the changes of nodes importance by its radius difference.Experiments show that the proposed node ranking algorithm can discriminate nodes accurately and objectively by their importance,and the proposed visualization method can show the changes of nodes relation over time more intuitively.Thirdly,a visualization prototype system of state evolution of dynamic complex networks is designed and implemented by integrating improved visualization methods above.