A modified evidential methodology of identifying influential nodes in weighted networks

How to identify influential nodes in complex networks is still an open hot issue. In the existing evidential centrality (EVC), node degree distribution in complex networks is not taken into consideration. In addition, the global structure information has also been neglected. In this paper, a new Evidential Semi-local Centrality (ESC) is proposed by modifying EVC in two aspects. Firstly, the Basic Probability Assignment (BPA) of degree generated by EVC is modified according to the actual degree distribution, rather than just following uniform distribution. BPA is the generation of probability in order to model uncertainty. Secondly, semi-local centrality combined with modified EVC is extended to be applied in weighted networks. Numerical examples are used to illustrate the efficiency of the proposed method.     

Community structure discovery method based on the Gaussian kernel similarity matrix

Community structure discovery in complex networks is a popular issue, and overlapping community structure discovery in academic research has become one of the hot spots. Based on the Gaussian kernel similarity matrix and spectral bisection, this paper proposes a new community structure discovery method. First, by adjusting the Gaussian kernel parameter to change the scale of similarity, we can find the corresponding non-overlapping community structure when the value of the modularity is the largest relatively. Second, the changes of the Gaussian kernel parameter would lead to the unstable nodes jumping off, so with a slight change in method of non-overlapping community discovery, we can find the overlapping community nodes. Finally, synthetic data, karate club and political books datasets are used to test the proposed method, comparing with some other community discovery methods, to demonstrate the feasibility and effectiveness of this method.     

Generalized function projective (lag, anticipated and complete) synchronization between two different complex networks with nonidentical nodes

Generalized function projective (lag, anticipated and complete) synchronization between two different complex networks with nonidentical nodes is investigated in this paper. Based on Barbalat’s lemma, some sufficient synchronization criteria are derived by applying the nonlinear feedback control. Although previous work studied function projective synchronization on complex dynamical networks, the dynamics of the nodes are coupled partially linear chaotic systems. In our work, the dynamics of the nodes of the complex networks are any chaotic systems without the limitation of the partial linearity. In addition, each network can be undirected or directed, connected or disconnected, and nodes in either network may have identical or different dynamics. The proposed strategy is applicable to almost all kinds of complex networks. Numerical simulations further verify the effectiveness and feasibility of the proposed synchronization method. Numeric evidence shows that the synchronization rate is sensitively influenced by the feedback strength, the time delay, the network size and the network topological structure.     

A novel criterion for cluster synchronization of complex dynamical networks with coupling time-varying delays

This paper investigates the cluster synchronization problem for the time-varying delays coupling networks with nonidentical delayed dynamical systems by using pinning control method. We derive some simple and useful criteria for cluster synchronization for any initial values through an effective feedback control scheme and propose an adaptive feedback strategy that adjusts automatically the coupling strength. Finally, some numerical examples are then given to illustrate the theoretical results.     

二分网上的靴襻渗流

靴襻渗流最早应用于统计物理学中研究磁铁因非磁性杂质导致磁有序的降低并最终消失的现象. 随着复杂网络研究的深入, 许多学者展开网络上的靴襻渗流研究. 在自然界中, 许多系统自然呈现出二分结构, 二分网络是复杂网络中的一种重要的网络模式. 本文通过建立动力学方程和计算机仿真模拟的方法研究二分网上的靴襻渗流, 关注的参数是二分网中两类节点初始的活跃比例和活跃阈值, 分别用f₁, f₂和Ω₁, Ω₂表示, 得到二分网两类节点终态活跃比例随初始活跃比例的变化会发生相变等结论. 同时 验证了动力学方程与仿真模拟的一致性.     

An improved GPS method with a new pseudo-peripheral nodes finder in finite element analysis

Finding pseudo-peripheral nodes with the largest eccentricity is important in matrix bandwidth and profile reduction algorithms in finite element analysis. A heuristic parameter, called the “width-depth ratio” and denoted by κ, is presented for finding the pseudo-peripheral nodes with larger pseudo-diameter compared with the GPS (Gibbs-Poole-Stockmeyer) pseudo-peripheral nodes finder. A novel nodes renumbering algorithm is thus developed by using our nodes finder based on GPS method. Simulations show that proposed nodes finder is reliable and effective in locating the proper pseudo-peripheral nodes with larger pseudo-diameters. A shielded microstrip line is given as an example to testify the ability of the proposed algorithm in application. The results, including time, pseudo-diameter, bandwidths and profiles, all indicate that our method is more competitive than GPS algorithm to be used as the nodes renumbering algorithm.     

MEAN CONVERGENCE OF HERMITE-FEJ(E)R TYPE INTERPOLATION ON AN ARBITRARY SYSTEM OF NODES

In this paper sufficient conditions for mean convergence and rate of convergence of Hermite-Fejer type interpolation in the Lp norm on an arbitrary system of nodes are presented.     

基于金刚石氮-空位色心自旋系综与超导量子电路混合系统的量子节点纠缠

量子纠缠是实现量子计算和量子通信的核心基础,本文提出了在金刚石氮-空位色心(NV centers)自旋系综与超导量子电路耦合的混合系统中实现两个分离量子节点之间纠缠的理论方案.在该混合系统中,把金刚石NV centers自旋系综和与之耦合的超导共面谐振器视为一个量子节点,两个量子节点之间通过一个空的超导共面谐振器连接.具有较长相干时间的NV centers自旋系综作为一个量子存储器,用于制备、存储和发送量子信息;易于外部操控的超导量子电路可执行量子逻辑门操作,快速调控量子信息.为了实现两个分离量子节点之间的纠缠,首先对系统的哈密顿量进行正则变换,将其等价为两个NV centers自旋系综与同一个超导共面谐振器之间的JC耦合;然后采用NV centers自旋-光子混合比特编码的方式,通过调节超导共面谐振器的谐振频率,精确控制体系演化时间,高保真度地实现了两个分离量子节点之间的量子纠缠.本方案还可以进一步扩展和集成,用于构建多节点纠缠的分布式量子网络.     

Ranking the spreading influence of nodes in complex networks: An extended weighted degree centrality based on a remaining minimum degree decomposition

Ranking the spreading influence of nodes is crucial for developing strategies to control the spreading process on complex networks. In this letter, we define, for the first time, a remaining minimum degree (RMD) decomposition by removing the node(s) with the minimum degree iteratively. Based on the RMD decomposition, a weighted degree (WD) is presented by utilizing the RMD indices of the nearest neighbors of a node. WD assigns a weight to each degree of this node, which can distinguish the contribution of each degree to the spreading influence. Further, an extended weighted degree (EWD) centrality is proposed by extending the WD of the nearest neighbors of a node. Assuming that the spreading process on networks follows the Susceptible-Infectious-Recovered (SIR) model, we perform extensive experiments on a series of synthetic and real networks to comprehensively evaluate the performance of EWD and other eleven representative measures. The experimental results show that EWD is a relatively efficient measure in running efficiency, it exposes an advantage in accuracy in the networks with a relatively small degree heterogeneity, as well as exposes a competitive performance in resolution.