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In recent years, multiplex networks are becoming a research focus in the domain of complex networks. Discovering significant correlations between layers in multiplex networks can provide an insight to their structures. In this study, we propose some methods to measure the dissimilarities of different layers in directed and weighted multiplex networks. The dissimilarity is defined on two levels: node level and layer level. The node dissimilarity is computed based on the distance of the probability distribution of its link weights vectors in different layers; and the layer-level dissimilarity is the weighted sum of the nodes’ dissimilarities. Furthermore, the dissimilarity is disintegrated into the connection-based dissimilarity and the weight-based dissimilarity, which represent the topological structure changes and the link weight changes, respectively. The proposed methods are applied to international trade networks. [ABSTRACT FROM AUTHOR]

Abstract: It is a fundamental issue to find a small subset of influential individuals in a complex network such that they can spread information to the largest number of nodes in the network. Though some heuristic methods, including degree centrality, betweenness centrality, closeness centrality, the k-shell decomposition method and a greedy algorithm, can help identify influential nodes, they have limitations for networks with community structure. This paper reveals a new measure for assessing the influence effect based on influence scope maximization, which can complement the traditional measure of the expected number of influenced nodes. A novel method for identifying influential nodes in complex networks with community structure is proposed. This method uses the information transfer probability between any pair of nodes and the k-medoid clustering algorithm. The experimental results show that the influential nodes identified by the k-medoid method can influence a larger scope in networks with obvious community structure than the greedy algorithm without reducing the expected number of influenced nodes. [Copyright &y& Elsevier]

Abstract: Time series is a very popular type of data which exists in many domains. Clustering time series data has a wide range of applications and has attracted researchers from a wide range of discipline. In this paper a novel algorithm for shape based time series clustering is proposed. It can reduce the size of data, improve the efficiency and not reduce the effects by using the principle of complex network. Firstly, one-nearest neighbor network is built based on the similarity of time series objects. In this step, triangle distance is used to measure the similarity. Of the neighbor network each node represents one time series object and each link denotes neighbor relationship between nodes. Secondly, the nodes with high degrees are chosen and used to cluster. In clustering process, dynamic time warping distance function and hierarchical clustering algorithm are applied. Thirdly, some experiments are executed on synthetic and real data. The results show that the proposed algorithm has good performance on efficiency and effectiveness. [Copyright &y& Elsevier]