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In this paper, we present the weighted iterated friendship graphs and study the trapping problem on the weighted iterated friendship graphs. It can be found that for 0 < r < 1 and r = 1 , the relationship between the average trapping time (ATT) and network size is sublinear and linear, respectively. By controlling the parameters of the weighted iterated friendship graphs, the models are changed to the self-similar weighted networks. The average shortest weighted path (ASWP) in the self-similar weighted friendship graphs is studied. The results show that when 0 < r < 1 , the ASWP is bounded, and when r = 1 , the ASWP is linearly related to the order of the networks. [ABSTRACT FROM AUTHOR]

Recently, the consensus problem has attracted extensive attention of researchers from different scientific communities. In this paper, we study the leader–follower network coherence (i.e. the mean steady-state variance of the deviation from the static value of the leader nodes) in the weighted recursive tree networks with assigning the leaders orderly in the initial state. For the weighted recursive tree networks model, we study the characteristic polynomial of the Laplacian submatrix and obtain the relationship for the eigenvalues in two successive generations. The analytical formula of the leader–follower network coherence related by the sum of the reciprocal of all eigenvalues of this submatrix is obtained. The result shows that more number of leaders and the greater weight factor lead to better consensus. [ABSTRACT FROM AUTHOR]

Fractal generally has self-similarity. Using the self-similarity of fractal, we can obtain some important theories about complex networks. In this paper, we concern the Vicsek fractal in three-dimensional space, which provides a natural generalization of Vicsek fractal. Concretely, the Vicsek fractal in three-dimensional space is obtained by repeatedly removing equilateral cubes from an initial equilateral cube of unit side length, at each stage each remaining cube is divided into n 3 smaller cubes of which (3 n − 2) are kept and the rest discarded, where n is odd. In addition, we obtain the skeleton network of the Vicsek fractal in three-dimensional space. Then we focus on weighted average geodesic distance of the Vicsek fractal in three-dimensional space. Take n = 5 as an example, we define a similar measure on the Vicsek fractal in three-dimensional space by weight vector and calculate the weighted average geodesic distance. At the same time, asymptotic formula of weighted average geodesic distance on the skeleton network is also obtained. Finally, the general formula of weighted average geodesic distance should be applicable to the models when n , the base of a power, is odd. [ABSTRACT FROM AUTHOR]

With the deepening of research on complex networks, many properties of complex networks are gradually studied, for example, the mean first-passage times, the average receive times and the trapping times. In this paper, we further study the average trapping time of the weighted directed treelike network constructed by an iterative way. Firstly, we introduce our model inspired by trade network, each edge e i , j in undirected network is replaced by two directed edges with weights w i , j and w j , i . Then, the trap located at central node, we calculate the weighted directed trapping time (WDTT) and the average weighted directed trapping time (AWDTT). Remarkably, the WDTT has different formulas for even generations and odd generations. Finally, we analyze different cases for weight factors of weighted directed treelike network. [ABSTRACT FROM AUTHOR]

In this paper, a 1D chain network with a reverse weighted edge is introduced. We focus on studying the relationships including the convergence rate and the length, the convergence rate and weight of adding reverse edge relationships. Laplacian characteristic determinant is calculated and subsequently, the sum of the reciprocals of all nonzero Laplacian eigenvalues is obtained. Hence, the analytic expression of global mean weighted first-passage time (GMWFPT) can be deduced. The obtained results show that there exists a linearly positive relationship between GMWFPT and the weight r. [ABSTRACT FROM AUTHOR]

The eigentime identity for random walks on the weighted networks is the expected time for a walker going from a node to another node. Eigentime identity can be studied by the sum of reciprocals of all nonzero Laplacian eigenvalues on the weighted networks. In this paper, we study the weighted (m , n) -flower networks with the weight factor r. We divide the set of the nonzero Laplacian eigenvalues into three subsets according to the obtained characteristic polynomial. Then we obtain the analytic expression of the eigentime identity H t + 1 of the weighted (m , n) -flower networks by using the characteristic polynomial of Laplacian and recurrent structure of Markov spectrum. We take m = 3 , n = 2 as example, and show that the leading term of the eigentime identity on the weighted (3 , 2) -flower networks obey superlinearly, linearly with the network size. [ABSTRACT FROM AUTHOR]

The multiple subdivision graph of a graph G , denoted by S n (G) , is the graph obtained by inserting n paths of length 2 replacing every edge of G. When n = 1 , S 1 (G) = S (G) is the subdivision graph of G. Let G 1 be a graph with n 1 vertices and m 1 edges, G 2 be a graph with n 2 vertices and m 2 edges. The quasi-corona SG-vertex join G 1 △ G 2 of G 1 and G 2 is the graph obtained from S (G 1) ∪ G 1 and n 1 copies of G 2 by joining every vertex of G 1 to every vertex of G 2 , and multiple SG-vertex join G 1 ⊙ G 2 is the graph obtained from S n (G 1) ∪ G 1 and G 2 by joining every vertex of G 1 to every vertex of G 2 . In this paper, we calculate analytic expression of characteristic polynomial of adjacency matrix of the above two types of joins of graphs for the case of G 1 being a regular graph. Then we obtain their adjacency spectra for the case of G 1 and G 2 being regular graphs. [ABSTRACT FROM AUTHOR]

More and more attention has focused on consensus problem in the study of complex networks. Many researchers investigated consensus dynamics in a linear dynamical system with additive stochastic disturbances. In this paper, we construct iterated line graphs of multi-subdivision graph by applying multi-subdivided-line graph operation. It has been proven that the network coherence can be characterized by the Laplacian spectrum of network. We study the recursion formula of Laplacian eigenvalues of the graphs. After that, we obtain the scalings of the first- and second-order network coherence. [ABSTRACT FROM AUTHOR]

Recently, the study of many kinds of weighted networks has received the attention of researchers in the scientific community. In this paper, first, a class of weighted star-composed networks with a weight factor is introduced. We focus on the network consistency in linear dynamical system for a class of weighted star-composed networks. The network consistency can be characterized as network coherence by using the sum of reciprocals of all nonzero Laplacian eigenvalues, which can be obtained by using the relationship of Laplacian eigenvalues at two successive generations. Remarkably, the Laplacian matrix of the class of weighted star-composed networks can be represented by the Kronecker product, then the properties of the Kronecker product can be used to obtain conveniently the corresponding characteristic roots. In the process of finding the sum of reciprocals of all nonzero Laplacian eigenvalues, the key step is to obtain the relationship of Laplacian eigenvalues at two successive generations. Finally, we obtain the main results of the first- and second-order network coherences. The obtained results show that if the weight factor is 1 then the obtained results in this paper coincide with the previous results on binary networks, otherwise the scalings of the first-order network coherence are related to the node number of attaching copy graph, the weight factor and generation number. Surprisingly, the scalings of the first-order network coherence are independent of the node number of initial graph. Consequently, it will open up new perspectives for future research. [ABSTRACT FROM AUTHOR]

In recent years, weighted networks have been extensively studied in various fields. This paper studies characteristic polynomial of adjacency or Laplacian matrix for weighted treelike networks. First, a class of weighted treelike networks with a weight factor is introduced. Then, the relationships of adjacency or the Laplacian matrix at two successive generations are obtained. Finally, according to the operation of the block matrix, we obtain the analytic expression of the characteristic polynomial of the adjacency or the Laplacian matrix. The obtained results lay the foundation for the future study of adjacency spectrum or Laplacian spectrum. [ABSTRACT FROM AUTHOR]

Complex networks have attracted a great deal of attention from scientific communities, and have been proven as a useful tool to characterize the topologies and dynamics of real and human-made complex systems. Laplacian spectrum of the considered networks plays an essential role in their network properties, which have a wide range of applications in chemistry and others. Firstly, we define one vertex–vertex graph. Then, we deduce the recursive relationship of its eigenvalues at two successive generations of the normalized Laplacian matrix, and we obtain the Laplacian spectrum for vertex–vertex graph. Finally, we show the applications of the Laplacian spectrum, i.e. first-order network coherence, second-order network coherence, Kirchhoff index, spanning tree, and Laplacian-energy-like. [ABSTRACT FROM AUTHOR]

Compared with traditional random walk, biased walks have been studied extensively over the past decade especially in the transport and communication networks. In this paper, we first introduce the weighted rose networks. Then, for the weighted rose networks we focus on two biased walks, maximal entropy walk and weight-dependent walk, and obtain the exact expressions of their stationary distributions and mean first-passage times. Finally, we find that the average receiving time for maximal entropy walk is a quadratic function of the weight parameter r while the average receiving time for weighted-dependent walk is a linear function of the weight parameter r. Meanwhile, for the maximal entropy walk, the smaller the value of r is, the more efficient the trapping process is. For the weighted-dependent walk, the larger the value of r (r < r 0 ≈ 2. 6) is, the more efficient for the weighted-dependent walk, the smaller the value of r (r > r 0 ≈ 2. 6) is, the more efficient for the weight-dependent walk. • Weighted rose networks are introduced. • Mean first-passage times for maximal entropy or weight-dependent walk is studied. • Average receiving time and global mean first-passage time are obtained. • Transmission efficiency of maximal entropy walk or weight-dependent walk. [ABSTRACT FROM AUTHOR]

In this paper, we study the trapping problem in the weighted scale-free triangulation networks with the growth factor m and the weight factor r. We propose two biased walks, one is standard weight-dependent walk only including the nearest-neighbor jumps, the other is mixed weight-dependent walk including both the nearest-neighbor and the next-nearest-neighbor jumps. For the weighted scale-free triangulation networks, we derive the exact analytic formulas of the average trapping time (ATT), the average of node-to-trap mean first-passage time over the whole networks, which measures the efficiency of the trapping process. The obtained results display that for two biased walks, in the large network, the ATT grows as a power-law function of the network size N t with the exponent, represented by ln (4 r + 4) ln (4 m) when r ≠ m − 1. Especially when the case of r = 1 and m = 2 , the ATT grows linear with the network size N t . That is the smaller the value of r , the more efficient the trapping process is. Furthermore, comparing the standard weight-dependent walk with mixed weight-dependent walk, the obtained results show that although the next-nearest-neighbor jumps have no main effect on the trapping process, they can modify the coefficient of the dominant term for the ATT. The smaller the value of probability parameter 𝜃 , the more efficient the trapping process for the mixed weight-dependent walk is. [ABSTRACT FROM AUTHOR]

Weighted folded hypercube is an charming variance of the famous hypercube and is superior to the weighted hypercube in many criteria. We mainly study the scaling of network coherence for the weighted folded hypercube that is controlled by a weight factor. Network coherence quantifies the steady-state variance of these fluctuations, and it can be regarded as a measure of robustness of the consensus process to the additive noise. If networks with small steady-state variance have better network coherence, it can be regarded as more robust to noise than networks with low coherence. We firstly calculate the spectra of weighted folded hypercube and obtain the leading terms of network coherence that are quantified as the sum and square sum of reciprocals of all nonzero Laplacian eigenvalues. Finally, the results show that network coherence depends on iterations and weight factor. Meanwhile, with larger order, the scatings of the first- and second-order network coherence of weighted folded hypercube decrease with the increasing of weight factor. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we consider the weighted Dyson hierarchical network, that is a weighted fully-connected network, where the pattern of weights is ruled by a weight factor. Given that the Laplacian operator is intrinsically implied in the analysis of dynamic processes occurring on complex networks, Laplacian spectrum allows addressing analytically a large class of problems. Exploiting the deterministic recursive structure, we are able to derive explicitly all eigenvalues and their corresponding eigenvectors of Laplacian matrix. Further, we derive exact solutions of the trapping time (TT) and average trapping time (ATT) on the weighted Dyson hierarchical network with weight-dependent walk. The obtained results show that TT and ATT are related to the weight factor of the weighted Dyson hierarchical network. Highlights • Model of weighted Dyson hierarchical network is introduced. • Laplacian spectrum of weighted Dyson hierarchical network is obtained. • Eigenvectors of Laplacian matrix are obtained. • The scalings of both TT and ATT can be controlled by the weight factor. [ABSTRACT FROM AUTHOR]

The consensus dynamics with additive stochastic disturbances are characterized by the network coherence, which is the robustness of consensus algorithms when the nodes are subject to external perturbations. In this paper, the research goal is to obtain the first- and second-order network coherence quantifying as the sum and square sum of reciprocals of all nonzero Laplacian eigenvalues. One innovation point of this paper is the structure of a family of the weighted recursive trees with weight factor. We mainly obtain the exact expressions and scalings of network coherence on the family of weighted recursive trees. The scalings of first-order network coherence with network size obey three laws along with the range of the weight factor, while the scalings of second-order network coherence obey four laws along with the range of the weight factor. In addition, the scalings of first- and second-order network coherence on our studied networks are smaller than those performed on other studied networks when 1 m + 1 < r ≤ 1. The obtained results indicate that the efficiency of network coherence on the weighted network has close relation to the weight distribution, and we can design a better weight distribution to make the coherence of network more efficient. [ABSTRACT FROM AUTHOR]

Abstract The eigenvalues of the normalized Laplacian matrix of a network provide information on its structural properties and some relevant dynamical aspects, in particular for weight-dependent walk. In order to get the eigentime identity for weight-dependent walk, we need to obtain the eigenvalues and their multiplicities of the Laplacian matrix. Firstly, the model of the weighted scale-free triangulation networks is constructed. Then, the eigenvalues and their multiplicities of transition weight matrix are presented, after the recursive relationship of those eigenvalues at two successive generations are given. Consequently, the Laplacian spectrum is obtained. Finally, the analytical expression of the eigentime identity, indicating that the eigentime identity grows sublinearly with the network order, is deduced. Highlights • Eigentime identity of the weighted scale-free triangulation networks. • Transition weight matrix for weight-dependent walk. • Recursive relationship of those eigenvalues of transition weight matrix. • Eigentime identity grows sublinearly with the network order. [ABSTRACT FROM AUTHOR]

In this paper, we study the trapping time in the weighted pseudofractal scale-free networks (WPSFNs) and the average shortest weighted path in the modified weighted pseudofractal scale-free networks (MWPSFNs) with the weight factor r. At first, for exceptional case with the trap fixed at a hub node for weight-dependent walk, we derive the exact analytic formulas of the trapping time through the structure of WPSFNs. The obtained rigorous solution shows that the trapping time approximately grows as a power-law function of the number of network nodes with the exponent represented by ln 2 + 4 r 2 + r ln 3 . Then, we deduce the scaling expression of the average shortest weighted path through the iterative process of the construction of MWPSFNs. The obtained rigorous solution shows that the scalings of average shortest weighted path with network size obey three laws along with the range of the weight factor. We provide a theoretical study of the trapping time for weight-dependent walk and the average shortest weighted path in a wide range of deterministic weighted networks. [ABSTRACT FROM AUTHOR]

Laplacian spectrum gives a lot of useful information about complex structural properties and relevant dynamical aspects, which has attracted the attention of mathematicians. We introduced the weighted scale-free network inspired by the binary scale-free network. First, the weighted scale-free network with a weight factor is constructed by an iterative way. In the next step, we use the definition of eigenvalue and eigenvector to obtain the recursive relationship of its eigenvalues and multiplicities at two successive generations. Through analysis of eigenvalues of transition weight matrix we find that multiplicities of eigenvalues 0 of transition matrix are different for the binary scale-free network and the weighted scale-free network. Then, we obtain the eigenvalues for the normalized Laplacian matrix of the weighted scale-free network by using the obtained eigenvalues of transition weight matrix. Finally, we show some applications of the Laplacian spectrum in calculating eigentime identity and Kirchhoff index. The leading term of these indexes are completely different for the binary and the weighted scale-free network. [ABSTRACT FROM AUTHOR]

Complex networks are omnipresent in science and in our real life, and have been the focus of intense interest. It is vital to research the impact of their characters on the dynamic progress occurring on complex networks for weight-dependent walk. In this paper, we first consider the weight-dependent walk on one kind of transfractal (or fractal) which is named the weighted transfractal (1 , 3) -flowers. And we pay attention to the first return time (FRT). We mainly calculate the mean and variance of FRT for a prescribed hub (i.e. the most concerned nodes) in virtue of exact probability generating function and its properties. Then, we obtain the mean and the secondary moment of the first return time. Finally, using the relationship among the variance, mean and the secondary moment, we obtain the variance of FRT and the scaling properties of the mean and variance of FRT on weighted transfractals (1 , 3) -flowers. [ABSTRACT FROM AUTHOR]

Much information about the structural properties and dynamical aspects of a network is measured by the eigenvalues of its normalized Laplacian matrix. In this paper, we aim to present a first study on the spectra of the normalized Laplacian matrix of weighted iterated quadrilateral graphs. We analytically obtain all the eigenvalues, as well as their multiplicities from two successive generations. As an example of application of these results, we then derive closed-form expressions for the multiplicative degree Kirchhoff index and the Kemeny's constant, as well as the number of weighted spanning trees. [ABSTRACT FROM AUTHOR]

It has been recently reported that trapping problem can characterize various dynamical processes taking place on complex networks. However, most works focused on the case of binary networks, and dynamical processes on weighted networks are poorly understood. In this paper, we study two kinds of biased walks including standard weight-dependent walk and mixed weight-dependent walk on the weighted scale-free treelike networks with a trap at the central node. Mixed weight-dependent walk including non-nearest neighbor jump appears in many real situations, but related studies are much less. By the construction of studied networks in this paper, we determine all the eigenvalues of the fundamental matrix for two kinds of biased walks and show that the largest eigenvalue has an identical dominant scaling as that of the average trapping time (ATT). Thus, we can obtain the leading scaling of ATT by a more convenient method and avoid the tedious calculation. The obtained results show that the weight factor has a significant effect on the ATT, and the smaller the value of the weight factor, the more efficient the trapping process is. Comparing the standard weight-dependent walk with mixed weight-dependent walk, although next-nearest-neighbor jumps have no main effect on the trapping process, they can modify the coefficient of the dominant term for the ATT. [ABSTRACT FROM AUTHOR]

For a network, knowledge of its Laplacian eigenvalues is central to understand its structure and dynamics. In this paper, we study the Laplacian spectra and their applications for a family of treelike networks. Firstly, in order to obtain the Laplacian spectra, we calculate the constant term and monomial coefficient of characteristic polynomial of the Laplacian matrix for a family of treelike networks. By using the Vieta theorem, we then obtain the sum of reciprocals of all nonzero eigenvalues of Laplacian matrix. Finally, we determine some interesting quantities that are related to the sum of reciprocals of all nonzero eigenvalues of Laplacian matrix, such as Kirchhoff index, global mean-first passage time (GMFPT). [ABSTRACT FROM AUTHOR]

This paper investigates consensus dynamics in a dynamical system with additive stochastic disturbances that is characterized as network coherence by using the Laplacian spectrum. We introduce a class of weighted networks based on a complete graph and investigate the first- and second-order network coherence quantifying as the sum and square sum of reciprocals of all nonzero Laplacian eigenvalues. First, the recursive relationship of its eigenvalues at two successive generations of Laplacian matrix is deduced. Then, we compute the sum and square sum of reciprocal of all nonzero Laplacian eigenvalues. The obtained results show that the scalings of first- and second-order coherence with network size obey four and five laws, respectively, along with the range of the weight factor. Finally, it indicates that the scalings of our studied networks are smaller than other studied networks when 1 d < r ≤ 1. [ABSTRACT FROM AUTHOR]

The eigenvalues of the normalized Laplacian of a graph provide information on its structural properties and also on some relevant dynamical aspects, in particular those related to weight-dependent walk. In this paper, we first present a study on the transition weight matrix of a weighted network. To get the eigentime identity for weight-dependent walk and weighted counting of spanning trees, we need to obtain all the eigenvalues and their multiplicities of the transition weight matrix. Then we obtain the recursive relationship of its eigenvalues at two successive generations of transition weight matrix. By substituting, we can obtain the relationship of normalized Laplacian matrix's eigenvalues at two successive generations. Using the relationship and Vietas formulas, we obtain the scalings of the eigentime identity. Afterwards, we classify normalized Laplacian matrix's eigenvalues and compute the product of all nonzero normalized Laplacian eigenvalues by the product recursive relationship. The product is used to obtain weighted counting of spanning trees. Finally, by weighted counting of spanning trees, we validate the obtained eigenvalues and their multiplicities. The obtained results show that the weight factor has a strong effect on the behavior of weight-dependent walks. [ABSTRACT FROM AUTHOR]

Complex networks have elicited considerable attention from scientific communities. This paper investigates consensus dynamics in a linear dynamical system with additive stochastic disturbances, which is characterized as network coherence by the Laplacian spectrum. Firstly, we introduce a class of weighted tree-like polymer networks with the weight factor. Then, we deduce the recursive relationship of the eigenvalues of Laplacian matrix at two successive generations. Finally, we calculate the first- and second-order network coherence quantifying as the sum and square sum of reciprocals of all nonzero Laplacian eigenvalues. The obtained results show that the scalings of first-order coherence with network size obey four laws along with the range of the weight factor and the scalings of second-order coherence with network size obey five laws along with the range of the weight factor. [ABSTRACT FROM AUTHOR]

Much information about the structural properties and dynamical aspects of a network is measured by the eigenvalues of its normalized Laplacian matrix. In this paper, we aim to present a study on the spectra of the normalized Laplacian of weighted tree-like fractals. We analytically obtain the relationship between the eigenvalues and their multiplicities for two successive generations. As an example of application of these results, we then derive closed-form expressions for their multiplicative Kirchhoff index and Kemeny’s constant. [ABSTRACT FROM AUTHOR]

Much information about the structural properties and dynamical aspects of a network is measured by the eigenvalues of its normalized Laplacian matrix. In this paper, we aim to present a first study on the spectra of the normalized Laplacian of weighted iterated triangulations of graphs. We analytically obtain all the eigenvalues, as well as their multiplicities from two successive generations. As an example of application of these results, we then derive closed-form expressions for their multiplicative Kirchhoff index, Kemeny's constant and number of weighted spanning trees. [ABSTRACT FROM AUTHOR]

In this paper, we first analytically calculate the eigenvalues of the transition matrix of a structure with very complex architecture and their multiplicities. We call this structure polymer network. Based on the eigenvalues obtained in the iterative manner, we then calculate the eigentime identity. We highlight two scaling behaviors (logarithmic and linear) for this quantity, strongly depending on the value of the weight factor. Finally, by making use of the obtained eigenvalues, we determine the weighted counting of spanning trees. [ABSTRACT FROM AUTHOR]

In this paper, we consider the entire mean first-passage time (EMFPT) with random walks for Cayley networks. We use Laplacian spectra to calculate the EMFPT. Firstly, we calculate the constant term and monomial coefficient of characteristic polynomial. By using the Vieta theorem, we then obtain the sum of reciprocals of all nonzero eigenvalues of Laplacian matrix. Finally, we obtain the scaling of the EMFPT for Cayley networks by using the relationship between the sum of reciprocals of all nonzero eigenvalues of Laplacian matrix and the EMFPT. We expect that our method can be adapted to other types of self-similar networks, such as vicsek networks, polymer networks. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the eigentime identities quantifying as the sum of reciprocals of all nonzero normalized Laplacian eigenvalues on a family of treelike networks and the polymer networks. Firstly, for a family of treelike networks, it is shown that all their eigenvalues can be obtained by computing the roots of several small-degree polynomials defined recursively. We obtain the scalings of the eigentime identity on a family of treelike with network size N n is N n ln N n . Then, for the polymer networks, we apply the spectral decimation approach to determine analytically all the eigenvalues and their corresponding multiplicities. Using the relationship between the generation and the next generation of eigenvalues we obtain the scalings of the eigentime identity on the polymer networks with network size N n is N n ln N n . By comparing the eigentime identities on these two kinds of networks, their scalings with network size N n are all N n ln N n . [ABSTRACT FROM AUTHOR]

In this paper, we first study the first-order network coherence, characterized by the entire mean first-passage time (EMFPT) for weight-dependent walk, on the weighted Cayley networks with the weight factor. The analytical formula of the EMFPT is obtained by the definition of the EMFPT. The obtained results show that the scalings of first-order coherence with network size obey four laws along with the range of the weight factor. Then, we study eigentime identity quantifying as the sum of reciprocals of all nonzero normalized Laplacian eigenvalues on the weighted Cayley networks with the weight factor. We show that all their eigenvalues can be obtained by calculating the roots of several small-degree polynomials defined recursively. The obtained results show that the scalings of the eigentime identity on the weighted Cayley networks obey two laws along with the range of the weight factor. [ABSTRACT FROM AUTHOR]

In this paper we study the weighted hierarchical graph which is generated from bipartite graph with N = n 1 + n 2 vertices, in which the weights of edges have been assigned to different values with certain scale. Firstly, we introduce the definition of the modified box dimension. Then for the weighted hierarchical graph we deduce the modified box dimension, dim M B ( { G n } n ∈ N ) = − log r N , depending on the weighted factor r and the number N of copies. Secondly, we mainly study their two average weighted receiving times (AWRTs), 〈 T 〉 I n and 〈 T 〉 II n , of the weighted hierarchical graph on random walk. We discuss two cases. In the case of n 1 n 2 r ≠ n 2 − n 1 , we deduce both AWRTs grow as a power-law function of the network size | V ( G n ) | with the postive exponent, represented by θ = log N ( N n 1 n 2 ) or θ = log N r = 1 − dim M B ( { G n } n ∈ N ) , which means that the bigger the value of the modified box dimension is, the slower the process of receiving information is. In the case of n 1 n 2 r = n 2 − n 1 , both AWRTs tend to constant ( if N < n 1 n 2 ) , the AWRTs grow with increasing order as log N | V ( G n ) | ( if N = n 1 n 2 ) , and both AWRTs grow as a power-law function of the network size | V ( G n ) | with the exponent, represented by θ = log N ( N n 1 n 2 ) > 0 ( if N > n 1 n 2 ) . [ABSTRACT FROM AUTHOR]

In this paper, based on actual road networks, a model of the non-homogeneous double-weighted fractal networks is introduced depending on the number of copies s and two kinds of weight factors w i , r i ( i = 1 , 2 , … , s ) . The double-weights represent the capacity-flowing weights and the cost-traveling weights, respectively. Denote by w i j F the capacity-flowing weight connecting the nodes i and j , and denote by w i j C the cost-traveling weight connecting the nodes i and j . Let w i j F be related to the weight factors w 1 , w 2 , … , w s , and let w i j C be related to the weight factors r 1 , r 2 , … , r s . Assuming that the walker, at each step, starting from its current node, moves to any of its neighbors with probability proportional to the capacity-flowing weight of edge linking them. The weighted time for two adjacency nodes is the cost-traveling weight connecting the two nodes. The average weighted receiving time (AWRT) is defined on the non-homogeneous double-weighted fractal networks. AWRT depends on the relationships of the number of copies s and two kinds of weight factors w i , r i ( i = 1 , 2 , … , s ) . The obtained remarkable results display that in the large network, the AWRT grows as a power-law function of the network size N g with the exponent, represented by θ = log s ( w 1 r 1 + w 2 r 2 + ⋯ + w s r s ) < 1 when w 1 r 1 + w 2 r 2 + ⋯ + w s r s ≠ 1 , which means that the smaller the value of w 1 r 1 + w 2 r 2 + ⋯ + w s r s is, the more efficient the process of receiving information is. Especially when w 1 r 1 + w 2 r 2 + ⋯ + w s r s = 1 , AWRT grows with increasing order N g as log N g or ( log N g ) 2 . In the classic fractal networks, the average receiving time (ART) grows with linearly with the network size N g . Thus, the non-homogeneous double-weighted fractal networks are more efficient than classic fractal networks in term of receiving information. [ABSTRACT FROM AUTHOR]

Fractal interpolation filter is proposed for the first time in the literatures to transform original signals. Using the multifractal detrended fluctuation analysis (MFDFA), the authors investigate how the filter affects the multifractal scaling properties for both artificial and traffic signals. Specifically, the authors compare the multifractal scaling properties of signals before and after the transforms. It is shown that the fractal interpolation filter changes slightly the maximum value of the multifractal spectrum, while the values of spectrum width and maximum point of spectrum are much more affected by vertical scaling factor. The multifractal spectrum shrinks dramatically after the fractal interpolation filter. The fractal exponents in the signal change dramatically for the negative values of vertical scaling factor while remain stable otherwise. Thus, an appropriate vertical scaling factor can be found in order to minimize the effects of filter when one uses the fractal interpolation filter. [ABSTRACT FROM AUTHOR]

We propose the entire mean weighted first-passage time (EMWFPT) for the first time in the literature. The EMWFPT is obtained by the sum of the reciprocals of all nonzero Laplacian eigenvalues on weighted networks. Simplified calculation of EMWFPT is the key quantity in the study of infinite families of weighted tree networks, since the weighted complex systems have become a fundamental mechanism for diverse dynamic processes. We base on the relationships between characteristic polynomials at different generations of their Laplacian matrix and Laplacian eigenvalues to compute EMWFPT. This technique of simplified calculation of EMWFPT is significant both in theory and practice. In this paper, firstly, we introduce infinite families of weighted tree networks with recursive properties. Then, we use the sum of the reciprocals of all nonzero Laplacian eigenvalues to calculate EMWFPT, which is equal to the average of MWFPTs over all pairs of nodes on infinite families of weighted networks. In order to compute EMWFPT, we try to obtain the analytical expressions for the sum of the reciprocals of all nonzero Laplacian eigenvalues. The key step here is to calculate the constant terms and the coefficients of first-order terms of characteristic polynomials. Finally, we obtain analytically the closed-form solutions to EMWFPT on the weighted tree networks and show that the leading term of EMWFPT grows superlinearly with the network size. [ABSTRACT FROM AUTHOR]

The multifractal analysis of one time series, e.g. crude oil, gold and exchange rate series, is often referred. In this paper, we apply the classical multifractal and mixed multifractal spectrum to study multifractal properties of crude oil, gold and exchange rate series and their inner relationships. The obtained results show that in general, the fractal dimension of gold and crude oil is larger than that of exchange rate (RMB against the US dollar), reflecting a fact that the price series in gold and crude oil are more heterogeneous. Their mixed multifractal spectra have a drift and the plot is not symmetric, so there is a low level of mixed multifractal between each pair of crude oil, gold and exchange rate series. [ABSTRACT FROM AUTHOR]

Based on the weighted Koch networks and the self-similarity of fractals, we present a family of weighted polygon Koch networks with a weight factor r ( 0 < r ≤ 1 ) . We study the average receiving time (ART) on weight-dependent walk (i.e., the walker moves to any of its neighbors with probability proportional to the weight of edge linking them), whose key step is to calculate the sum of mean first-passage times (MFPTs) for all nodes absorpt at a hub node. We use a recursive division method to divide the weighted polygon Koch networks in order to calculate the ART scaling more conveniently. We show that the ART scaling exhibits a sublinear or linear dependence on network order. Thus, the weighted polygon Koch networks are more efficient than expended Koch networks in receiving information. Finally, compared with other previous studies’ results (i.e., Koch networks, weighted Koch networks), we find out that our models are more general. [ABSTRACT FROM AUTHOR]

In this paper, based on the un-weight hierarchical networks, a family of weighted hierarchical networks are introduced, the weight factor is denoted by . The weighted hierarchical networks depend on the number of nodes in complete bipartite graph, denoted by , and . Assume that the walker, at each step, starting from its current node, moves to any of its neighbors with probability proportional to the weight of edge linking them. We deduce the analytical expression of the average receiving time (ART). The obtained remarkable results display two conditions. In the large network, when , the ART grows as a power-law function of the network size with the exponent, represented by , . This means that the smaller the value of , the more efficient the process of receiving information. When , the ART grows with increasing order as or . [ABSTRACT FROM AUTHOR]

Motivated by the empirical observation in airport networks and metabolic networks, we introduce the model of the recursive weighted Koch networks created by the recursive division method. As a fundamental dynamical process, random walks have received considerable interest in the scientific community. Then, we study the recursive weighted Koch networks on random walk i.e., the walker, at each step, starting from its current node, moves uniformly to any of its neighbours. In order to study the model more conveniently, we use recursive division method again to calculate the sum of the mean weighted first-passing times for all nodes to absorption at the trap located in the merging node. It is showed that in a large network, the average weighted receiving time grows sublinearly with the network order. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the traffic time series for volume data observed on the Guangshen highway. We introduce a multifractal detrended fluctuation analysis based on fractal fitting (MFDFA-FF), which is one of the most effective methods to detect long-range correlations of time series. Through effective detecting of long-range correlations, highway volume can be predicted more accurately. In order to get a better detrend effect, we use fractal fitting to replace polynomial fitting in detrend process, the result shows that fractal fitting can get a better detrend effect than polynomial fitting and the MFDFA-FF method can achieve a more accurate research result. Then we introduce the Legendre spectrum to detect the multifractal property characterized by the long-range correlation and multifractality of Guangshen highway volume data. [ABSTRACT FROM AUTHOR]

We introduce weighted tetrahedron Koch networks with infinite weight factors, which are generalization of finite ones. The term of weighted time is firstly defined in this literature. The mean weighted first-passing time (MWFPT) and the average weighted receiving time (AWRT) are defined by weighted time accordingly. We study the AWRT with weight-dependent walk. Results show that the AWRT for a nontrivial weight factor sequence grows sublinearly with the network order. To investigate the reason of sublinearity, the average receiving time (ART) for four cases are discussed. [ABSTRACT FROM AUTHOR]

Motivated by an evolving model of online bipartite networks, we introduce a model of weighted bipartite evolving networks. In this model, there are two disjoint sets of nodes, called user node set and object node set. Edges only exist between two disjoint sets. Edge weights represent the usage amount between a couple of user node and object node. This model not only clinches the bipartite networks’ internal mechanism of network growth, but also takes into account the object strength deterioration over time step. User strength and object strength follow power-law distributions, respectively. The weighted bipartite evolving networks have scare-free property in certain situations. Numerical simulations results agree with the theoretical analyses. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a model of the double-weighted Koch networks based on actual road networks depending on the two weight factors w,r ∈ (0, 1]. The double weights represent the capacity-flowing weight and the cost-traveling weight, respectively. Denote by the capacity-flowing weight connecting the nodes i and j, and denote by the cost-traveling weight connecting the nodes i and j. Let be related to the weight factor w, and let be related to the weight factor r. This paper assumes that the walker, at each step, starting from its current node, moves to any of its neighbors with probability proportional to the capacity-flowing weight of edge linking them. The weighted time for two adjacency nodes is the cost-traveling weight connecting the two nodes. We define the average weighted receiving time (AWRT) on the double-weighted Koch networks. The obtained result displays that in the large network, the AWRT grows as power-law function of the network order with the exponent, represented by θ(w,r) = ½ log2(1 + 3wr). We show that the AWRT exhibits a sublinear or linear dependence on network order. Thus, the double-weighted Koch networks are more efficient than classic Koch networks in receiving information. [ABSTRACT FROM AUTHOR]

A model of fractal hierarchical structures that share the property of non-homogeneous weighted networks is introduced. These networks can be completely and analytically characterized in terms of the involved parameters, i.e., the size of the original graph Nk and the non-homogeneous weight scaling factors r1, r2, · · · rM. We also study the average weighted shortest path (AWSP), the average degree and the average node strength, taking place on the non-homogeneous hierarchical weighted networks. Moreover the AWSP is scrupulously calculated. We show that the AWSP depends on the number of copies and the sum of all non-homogeneous weight scaling factors in the infinite network order limit. [ABSTRACT FROM AUTHOR]

Many real-world networks, ranging from the world trade web to the Internet network, have been described by multi-local-worlds. It is obvious that the nodes within a local world are much more connected to each other than to the others outside the local world. A multi-local-world model can capture and describe these real-world networks' topological properties. Based on the local-world model, a weighted multi-local-world evolving network model is presented. This model combines selected nodes with preferential attachment and three kinds of local changes of weights. Using a rate equation and the mean-field method, we study the network's properties: the weight distribution and the strength distribution. We theoretically prove that the weight distribution and the strength distribution follow a power-law distribution in some conditions. Numerical simulations are in agreement with the theoretical results. [ABSTRACT FROM AUTHOR]

A dynamic weight evolution network with preferential attachment is introduced. The network includes two significant characteristics. (i) Topological growth: triggered by newly added node with M links at each time step, each new edge carries an initial weight growing nonlinearly with time. (ii) Weight dynamics: the weight between two existing nodes experiences increasing or decreasing in a nonlinear way. By using continuum theory and mean-field method, we study the strength, the degree, the weight and their distributions. We find that the distributions exhibit a power-law feature. In particular, the relationship between the degree and the strength is nonlinear, and the power-law exponents of the three are the same. All the theoretical predictions are successfully contrasted with numerical simulations. [ABSTRACT FROM AUTHOR]