Optimal weighting scheme and the role of coupling strength against load failures in degree-based weighted interdependent networks

The optimal weighting scheme and the role of coupling strength against load failures on symmetrically and asymmetrically coupled interdependent networks were investigated. The degree-based weighting scheme was extended to interdependent networks, with the flow dynamics dominated by global redistribution based on weighted betweenness centrality. Through contingency analysis of one-node removal, we demonstrated that there still exists an optimal weighting parameter on interdependent networks, but it might shift as compared to the case in isolated networks because of the break of symmetry. And it will be easier for the symmetrically and asymmetrically coupled interdependent networks to achieve robustness and better cost configuration against the one-node-removal-induced cascade of load failures when coupling strength was weaker. Our findings might have great generality for characterizing load-failure-induced cascading dynamics in real-world degree-based weighted interdependent networks.     

Cascading dynamics with local weighted flow redistribution in interdependent networks

We study load cascading dynamics in a system composed of coupled interdependent networks while adopting a local weighted flow redistribution rule. We find that when the intra- or inter-connectivity increases, robustness against the cascade of load failures in the symmetrically coupled interdependent networks increases. In addition, when a failed link has to first split its flow asymmetrically to its neighbouring link groups according to the link types, even though there exists an optimal split, the robustness is lowered in contrast with the non-split situation. Furthermore, the optimal weighting mechanism in an isolated network no longer holds in interdependent networks. Finally, robustness against the cascade of load failures is not guaranteed to increase by making the distribution of the degree of intra-connectivity broader. We confirm these phenomena by theoretical analysis based on mean-field theory. Our findings might have great implications for preventing load-failure-induced local cascades in symmetrically coupled interdependent networks.     

Geographical effect on weighted network synchronisation

The propensity for synchronisation of weighted and asymmetrical networks is investigated. The weighting scheme is employed when connection strengths are weighted according to local information. The weighting schemes of different level of information restriction are compared. It is found that the optimal network synchronisability is obtained when the weighting scheme is based on softly restricted information. It is even better than schemes based on global information. This implies that “more knowledge, better network synchronisability” is not always true. This is mainly due to a partial synchronisation phenomenon taking place at a cluster level. The finding may be helpful to gain a better understanding of why synchronisation is widely observed in many real large-scale systems while each node is aware of limited information about only a small part of a network.     

基于负荷局域择优重新分配原则的复杂网络上的相继故障

相继故障普遍存在现实的网络系统中,为了更好地探讨复杂网络抵制相继故障的全局鲁棒性,采用网络中节点j上的初始负荷为L_j=k^α_j（k_j为节点j的度）的形式,并基于崩溃节点上负荷的局域择优重新分配的原则,提出了一个新的相继故障模型.依据新的度量网络鲁棒性的指标,探讨了4种典型复杂网络上的相继故障现象.数值模拟表明, 关键词： 相继故障 复杂网络 关键阈值 相变     

Attack vulnerability of scale-free networks due to cascading failures

In this paper, adopting the initial load of a node i to be with k_i being the degree of the node i, we propose a cascading model based on a load local redistribution rule and examine cascading failures on the typical network, i.e., the BA network with the scale-free property. We find that the BA scale-free network reaches the strongest robustness level in the case of α=1 and the robustness of the network has a positive correlation with the average degree 〈k〉, where the robustness is quantified by a transition from normal state to collapse. In addition, we further discuss the effects of two different attacks for the robustness against cascading failures on our cascading model and find an interesting result, i.e., the effects of two different attacks, strongly depending to the value α. These results may be very helpful for real-life networks to avoid cascading-failure-induced disasters.     

Refinement of the community detection performance by weighted relationship coupling

The complexity of many community detection algorithms is usually an exponential function with the scale which hard to uncover community structure with high speed. Inspired by the ideas of the famous modularity optimization, in this paper, we proposed a proper weighting scheme utilizing a novel k-strength relationship which naturally represents the coupling distance between two nodes. Community structure detection using a generalized weighted modularity measure is refined based on the weighted k-strength matrix. We apply our algorithm on both the famous benchmark network and the real networks. Theoretical analysis and experiments show that the weighted algorithm can uncover communities fast and accurately and can be easily extended to large-scale real networks.     

Proactive robustness control of heterogeneously loaded networks

A proactive measure to increase the robustness of heterogeneously loaded networks against cascades of overload failures is proposed. It is based on load-dependent weights. Compared to simple hop weights, respective shortest flow paths turn a previously heterogeneous load distribution into a more homogeneous one for the nodes and links of the network. The use of these flow paths increases the networks robustness and at the same time reduces the investment costs into the networks capacity layout. These findings are of relevance for critical infrastructures like communication and transportation networks.     

An LCOR model for suppressing cascading failure in weighted complex networks

Based on the relationship between capacity and load, cascading failure on weighted complex networks is investigated, and a load-capacity optimal relationship (LCOR) model is proposed in this paper. Compared with three other kinds of load-capacity linear or non-linear relationship models in model networks as well as a number of real-world weighted networks including the railway network, the airports network and the metro network, the LCOR model is shown to have the best robustness against cascading failure with less cost. Furthermore, theoretical analysis and computational method of its cost threshold are provided to validate the effectiveness of the LCOR model. The results show that the LCOR model is effective for designing real-world networks with high robustness and less cost against cascading failure.     

Preservation of network degree distributions from non-uniform failures

There has been a considerable amount of interest in recent years on the robustness of networks to failures. Many previous studies have concentrated on the effects of node and edge removals on the connectivity structure of a static network; the networks are considered to be static in the sense that no compensatory measures are allowed for recovery of the original structure. Real world networks such as the world wide web, however, are not static and experience a considerable amount of turnover, where nodes and edges are both added and deleted. Considering degree-based node removals, we examine the possibility of preserving networks from these types of disruptions. We recover the original degree distribution by allowing the network to react to the attack by introducing new nodes and attaching their edges via specially tailored schemes. We focus particularly on the case of non-uniform failures, a subject that has received little attention in the context of evolving networks. Using a combination of analytical techniques and numerical simulations, we demonstrate how to preserve the exact degree distribution of the studied networks from various forms of attack.     

基于可调负载重分配的无标度网络连锁效应分析

为了深入研究复杂网络抵制连锁故障的全局鲁棒性,针对现实网络上的负载重分配规则常常是介于全局分配与最近邻分配、均匀分配与非均匀分配的特点,围绕负荷这一影响连锁故障发生和传播最重要的物理量以及节点崩溃后的动力学过程,提出了一种可调负载重分配范围与负载重分配异质性的复杂网络连锁故障模型,并分析了该模型在无标度网络上的连锁故障条件.数值模拟获得了复杂网络抵制连锁故障的鲁棒性与模型中参数的关系.此外,基于网络负载分配规则的分析以及理论解析的推导,验证了数值模拟结论,也证明在最近邻与全局分配两种规则下都存在负载分配均匀性参数等于初始负荷强度参数即β=τ使得网络抵御连锁故障的能力最强.     

The role of degree-weighted couplings in the synchronous onset of Kuramoto oscillator networks

This paper investigates the role of asymmetrical degree-dependent weighted couplings in synchronization of a network of Kuramoto oscillators, where the conditions of coupling criticality for the onset of phase synchronization in degree-weighted complex networks are arrived at. The numerical simulations visualize that for networks having power-law or exponential degree distributions, asymmetrical degree-weighted couplings (with increasing weighting exponent β) increases the critical coupling to achieve the onset of phase synchronization in the networks.     

Controllable preparation of entangled coherent states with superconducting system

Utilizing a current-biased Josephson junction (CBJJ) as a tunable coupler for superconducting transmission line resonators (TLRs), we propose a potentially practical scheme to create entangled coherent states of the two TLR modes. Then, the influence of TLRs decay on the prepared entangled states is analyzed. And an interesting phenomenon that even entangled coherent states are robustness against decay with small α is found. At last, the experimental feasibility and the challenge of our schemes have been discussed.     

The effect of weight on community structure of networks

The effect of weight on community structures is investigated in this paper. We use weighted modularity Q^w$Q^w$ to evaluate the partitions and weighted extremal optimization algorithm to detect communities. Starting from empirical and idealized weighted networks, the matching between weights and edges are disturbed. Then using similarity function S to measure the difference between community structures, it is found that the redistribution of weights does strongly affect the community structure especially in dense networks. This indicates that the community structure in networks is a suitable property to reflect the role of weight.     

Dynamical robustness analysis of weighted complex networks

Robustness of weighted complex networks is analyzed from nonlinear dynamical point of view and with focus on different roles of high-degree and low-degree nodes. We find that the phenomenon for the low-degree nodes being the key nodes in the heterogeneous networks only appears in weakly weighted networks and for weak coupling. For all other parameters, the heterogeneous networks are always highly vulnerable to the failure of high-degree nodes; this point is the same as in the structural robustness analysis. We also find that with random inactivation, heterogeneous networks are always more robust than the corresponding homogeneous networks with the same average degree except for one special parameter. Thus our findings give an integrated picture for the dynamical robustness analysis on complex networks.     

An adaptive central-upwind weighted essentially non-oscillatory scheme

In this work, an adaptive central-upwind 6th-order weighted essentially non-oscillatory (WENO) scheme is developed. The scheme adapts between central and upwind schemes smoothly by a new weighting relation based on blending the smoothness indicators of the optimal higher order stencil and the lower order upwind stencils. The scheme achieves 6th-order accuracy in smooth regions of the solution by introducing a new reference smoothness indicator. A number of numerical examples suggest that the present scheme, while preserving the good shock-capturing properties of the classical WENO schemes, achieves very small numerical dissipation.     

Weighted Similarity and Core-User-Core-Item Based Recommendations

In traditional recommendation algorithms, the users and/or the items with the same rating scores are equally treated. In real world, however, a user may prefer some items to other items and some users are more loyal to a certain item than other users. In this paper, therefore, we propose a weighted similarity measure by exploiting the difference in user-item relationships. In particular, we refer to the most important item of a user as his core item and the most important user of an item as its core user. We also propose a Core-User-Item Solver (CUIS) to calculate the core users and core items of the system, as well as the weighting coefficients for each user and each item. We prove that the CUIS algorithm converges to the optimal solution efficiently. Based on the weighted similarity measure and the obtained results by CUIS, we also propose three effective recommenders. Through experiments based on real-world data sets, we show that the proposed recommenders outperform corresponding traditional-similarity based recommenders, verify that the proposed weighted similarity can improve the accuracy of the similarity, and then improve the recommendation performance.     

A model for cascading failures in scale-free networks with a breakdown probability

Considering that not all overload nodes will be removed from networks due to some effective measures to protect them, we propose a new cascading model with a breakdown probability. Adopting the initial load of a node j to be L_j=[k_j(∑_m∈Γjk_m)]^α with k_j and Γ_j being the degree of the node j and the set of its neighboring nodes, respectively, where α is a tunable parameter, we investigate the relationship between some parameters and universal robustness characteristics against cascading failures on scale-free networks. According to a new measure originated from a phase transition from the normal state to collapse, the numerical simulations show that Barabási-Albert (BA) networks reach the strongest robustness level against cascading failures when the tunable parameter α=0.5, while not relating to the breakdown probability. We furthermore explore the effect of the average degree 〈k〉 for network robustness, thus obtaining a positive correlation between 〈k〉 and network robustness. We then analyze the effect of the breakdown probability on the network robustness and confirm by theoretical predictions this universal robustness characteristic observed in simulations. Our work may have practical implications for controlling various cascading-failure-induced disasters in the real world.     

Symmetry-based structure entropy of complex networks

Precisely quantifying the heterogeneity or disorder of network systems is important and desired in studies of behaviors and functions of network systems. Although various degree-based entropies have been available to measure the heterogeneity of real networks, heterogeneity implicated in the structures of networks can not be precisely quantified yet. Hence, we propose a new structure entropy based on automorphism partition. Analysis of extreme cases shows that entropy based on automorphism partition can quantify the structural heterogeneity of networks more precisely than degree-based entropies. We also summarized symmetry and heterogeneity statistics of many real networks, finding that real networks are more heterogeneous in the view of automorphism partition than what have been depicted under the measurement of degree-based entropies; and that structural heterogeneity is strongly negatively correlated to symmetry of real networks.     

Error and attack tolerance of evolving networks with local preferential attachment

Networks generated by local-world evolving network model display a transition from exponential network to power-law network with respect to connectivity distribution. We investigate statistical properties of the evolving networks and the responses of these networks under random errors and intentional attacks. It has been found that local world size M has great effect on the network's heterogeneity, thus leading to transitional behaviors in network's robustness against errors and attacks. Numerical results show that networks constructed with local preferential attachment mechanism can maintain the robustness of scale-free networks under random errors and concurrently improve reliance against targeted attacks on highly connected nodes.     

Coded Caching for Broadcast Networks with User Cooperation

Caching technique is a promising approach to reduce the heavy traffic load and improve user latency experience for the Internet of Things (IoT). In this paper, by exploiting edge cache resources and communication opportunities in device-to-device (D2D) networks and broadcast networks, two novel coded caching schemes are proposed that greatly reduce transmission latency for the centralized and decentralized caching settings, respectively. In addition to the multicast gain, both schemes obtain an additional cooperation gain offered by user cooperation and an additional parallel gain offered by the parallel transmission among the server and users. With a newly established lower bound on the transmission delay, we prove that the centralized coded caching scheme is order-optimal, i.e., achieving a constant multiplicative gap within the minimum transmission delay. The decentralized coded caching scheme is also order-optimal if each user’s cache size is larger than a threshold which approaches zero as the total number of users tends to infinity. Moreover, theoretical analysis shows that to reduce the transmission delay, the number of users sending signals simultaneously should be appropriately chosen according to the user’s cache size, and always letting more users send information in parallel could cause high transmission delay.