Dynamics of load entropy during cascading failure propagation in scale-free networks

In this Letter, we introduce the concept of load entropy, which can be an average measure of a network's heterogeneity in the load distribution. Then we investigate the dynamics of load entropy during failure propagation using a new cascading failures load model, which can represent the node removal mechanism in many real-life complex systems. Simulation results show that in the early stage of failure propagation the load entropy for a larger cascading failure increases more sharply than that for a smaller one, and consequently the cascading failure with a larger damage can be identified at the early stage of failure propagation according to the load entropy. Particularly, load entropy can be used as an index to be optimized in cascading failures control and defense in many real-life complex networks.     

Effects of the cascading failures on scale-free traffic networks

In this paper, we consider the artificial scale-free traffic network with dynamic weights (cost) and focus on how the removal strategies (flow-based removal, betweenness-based removal and mix-based removal) affect the damage of cascading failures based on the user-equilibrium (UE) assignment, which ensures the balance of flow on the traffic network. Experiment simulation shows that different removal strategies can bring large dissimilarities of the efficiency and damage after the intentional removal of an edge. We show that the mix-based removal of a single edge might reduce the damage of cascading failures and delay the breakdown time, especially for larger reserve capacity coefficient α. This is particularly important for real-world networks with a highly hetereogeneous distribution of flow, i.e., traffic and transportation networks, logistics networks and electrical power grids.     

Comparison of cascading failures in small-world and scale-free networks subject to vertex and edge attacks

Complex networks may undergo a global cascade of overload failures when a single highly loaded vertex or edge is intentionally attacked. Here we use the recent load model of cascading failures to investigate the performance of the small-world (SW) and scale-free (SF) networks subject to deliberate attacks on vertex and edge. Simulation results suggest that compared with the SW network, the SF network is more vulnerable to deliberate vertex attacks and more robust to deliberate edge attacks. In the SF network, deliberate vertex attacks can result in larger cascading failures than deliberate edge attacks; however, in the SW network the situation is opposite. Furthermore, with the increase of the rewiring probability the SW network becomes more and more robust to deliberate vertex and edge attacks.     

Mitigation strategies on scale-free networks against cascading failures

According to the dynamic characteristics of the cascading propagation, we introduce a mitigation mechanism and propose four mitigation methods on four types of nodes. By the normalized average avalanche size and a new measure, we demonstrate the efficiencies of the mitigation strategies on enhancing the robustness of scale-free networks against cascading failures and give the order of the effectiveness of the mitigation strategies. Surprisingly, we find that only adopting once mitigation mechanism on a small part of the overload nodes can dramatically improve the robustness of scale-free networks. In addition, we also show by numerical simulations that the optimal mitigation method strongly depends on the total capacities of all nodes in a network and the distribution of the load in the cascading model. Therefore, according to the protection strength for scale-free networks, by the distribution of the load and the protection price of networks, we can reasonably select how many nodes and which mitigation method to efficiently protect scale-free networks at the lower price. These findings may be very useful for avoiding various cascading-failure-induced disasters in the real world and for leading to insights into the mitigation of cascading failures.     

Robustness of networks against cascading failures

Inspired by other related works, this paper proposes a non-linear load-capacity model against cascading failures, which is more suitable for real networks. The simulation was executed on the B-A scale-free network, E-R random network, Internet AS level network, and the power grid of the western United States. The results show that the model is feasible and effective. By studying the relationship between network cost and robustness, we find that the model can defend against cascading failures better and requires a lower investment cost when higher robustness is required.     

Direction coherence in scale-free lattices of chaotic maps

We considered a coupled chaotic logistic map lattice exhibiting the scale-free property: the outreach connectivity of each node obeys a power-law distribution. We analyzed a weak form of coherent spatio-temporal behavior (direction coherence) which presents features common to completely synchronized states, like a transitional behavior with intermittent bursting. We studied such phenomena and their dependence on the parameters of the coupled scale-free lattice. Prospective applications in neuronal networks are emphasized.     

Tolerance of edge cascades with coupled map lattices methods

This paper studies the cascading failure on random networks and scale-free networks by introducing the tolerance parameter of edge based on the coupled map lattices methods. The whole work focuses on investigating some indices including the number of failed edges, dynamic edge tolerance capacity and the perturbation of edge. In general, it assumes that the perturbation is attributed to the normal distribution in adopted simulations. By investigating the effectiveness of edge tolerance in scale-free and random networks, it finds that the larger tolerance parameter λ can more efficiently delay the cascading failure process for scale-free networks than random networks. These results indicate that the cascading failure process can be effectively controlled by increasing the tolerance parameter λ. Moreover, the simulations also show that, larger variance of perturbation can easily trigger the cascading failures than the smaller one. This study may be useful for evaluating efficiency of whole traffic systems, and for alleviating cascading failure in such systems.     

Understanding the cascading failures in Indian power grids with complex networks theory

Two huge blackouts, occurred separately on 30 and 31 July 2012 in India, spread over half the country when three of its five regional grids collapsed, leaving hundreds of millions of people without government-supplied electricity and ringing once again alarm bells with security problems in electric power grid systems. The first investigation reveals that the outage of the second (backup) 400 kV Bina–Gwalior–Agra line on 29 July 2012 led to the cascading failure through the grid, which can be simulated and explained from the perspective of the complex networks theory. In this paper, a new model of a power grid involving the active and reactive power loads is proposed and then used to analyze the cascading behavior of power grids, which is also used to explain the reason of the blackout happening in India. Furthermore, some strategic advices are given for improving the stability and security of power grids, especially Indian power grids.     

Traffic dynamics in scale-free networks with tunable strength of community structure

In this paper we systematically investigate the impact of community structure on traffic dynamics in scale-free networks based on local routing strategy. A growth model is introduced to construct scale-free networks with tunable strength of community structure, and a packet routing strategy with a parameter α is used to deal with the navigation and transportation of packets simultaneously. Simulations show that the maximal network capacity stands at α=−1 in the case of identical vertex capacity and monotonously decreases with the strength of community structure which suggests that the networks with fuzzy community structure (i.e., community strength is weak) are more efficient in delivering packets than those with pronounced community structure. To explain these results, the distribution of packets of each vertex is carefully studied. Our results indicate that the moderate strength of community structure is more convenient for the information transfer of real complex systems.     

Dynamical behavior of the multiplicative diffusion coupled map lattices

We report a dynamical study of multiplicative diffusion coupled map lattices with the coupling between the elements only through the bifurcation parameter of the mapping function. We discuss the diffusive process of the lattice from an initially random distribution state to a homogeneous one as well as the stable range of the diffusive homogeneous attractor. For various coupling strengths we find that there are several types of spatiotemporal structures. In addition, the evolution of the lattice into chaos is studied. A largest Lyapunov exponent and a spatial correlation function have been used to characterize the dynamical behavior. (c) 1996 American Institute of Physics.     

Collective behavior in coupled chaotic map lattices with random perturbations

Numerical simulations of coupled map lattices with non-local interactions (i.e., the coupling of a given map occurs with all lattice sites) often involve a large computer time if the lattice size is too large. In order to study dynamical effects which depend on the lattice size we considered the use of small truncated lattices with random inputs at their boundaries chosen from a uniform probability distribution. This emulates a “thermal bath”, where deterministic degrees of freedom exhibiting chaotic behavior are replaced by random perturbations of finite amplitude. We demonstrate the usefulness of this idea to investigate the occurrence of completely synchronized chaotic states as the coupling parameters are varied. We considered one-dimensional lattices of chaotic logistic maps at outer crisis x→4x(1−x).     

Epidemic spreading in a scale-free network of regular lattices

The susceptible-infected-susceptible (SIS) epidemics in a scale-free network in which each node is a square lattice itself is investigated through large-scale computer simulations. The model combines a local contact process among individuals in a node (or city) with stochastic long-range infections due to people traveling between cities interconnected by the national transportation scale-free network. A nonzero epidemic threshold is found and it is approached with a power-law behavior by the density of infected individuals, as observed in the small-world network of Watts and Strogatz. Also, the epidemic propagation follows a 1/f$1/f$, hierarchical dynamics from the highly connected square lattices to the smaller degree nodes in outbreaks with sizes distributed accordingly a Gaussian function.     

耦合映象格子系统的相互同步化

研究了两个耦合格子动力系统的非线性相互作用。当两个耦合格子系统一样时,则导致完全同步化。而当两个耦合格子系统的参数不一样或者这两个系统不相同时,则导致广义同步化。计算了Lyapunov指数谱。     

Traffic of packets with non-homogeneously selected destinations in scale-free network

In this paper, we study the information traffic flow in communication networks with scale-free topology. We consider the situation arising when packets are delivered to non-homogeneously selected destinations. It is found that the network capacity R_c increases with the increase of 〈k〉 (average degree of destination nodes) under local routing strategy. In contrast, R_c is essentially independent of 〈k〉 under shortest path strategy. Based on this finding, an integrated routing strategy that can enhance network capacity is proposed by combining the two strategies.     

Onset of power-law scaling behavior in idiotypic random and scale-free networks

We numerically study the dynamics of model immune networks with random and scale-free topologies. We observe that a memory state is reached when the antigen is attached to the most connected sites of the network, whereas a percolation state may occur when the antigen attaches to the less connected sites. For increasing values of the connectivity of the antibody directly binded to the antigen, its population converges exponentially to the asymptotic value of the memory state. On the other hand, the next-nearest populations evolve slowly as power-laws towards the virgin-like state.     

The impact of connection density on scale-free distribution in random networks

Preferential attachment is considered as a fundamental mechanism that contributes to the scale-free characteristics of random networks, which include growth and non-growth networks. There exist some situations of non-growth random networks, particularly for very sparse or dense networks, where preferential attachments cannot consequentially result in true scale-free features, but only in scale-free-like appearances. This phenomenon implies that, a close relationship exists between the connection density p$p$ and the scaling. In this study, we propose a self-organized model with constant network size to study the phenomenon. We show analytically and numerically that there exists a certain critical point _pc$p_c$. Only when p=_pc$p=p_c$, the random network evolves into steady scale-free state. Otherwise, the network exhibits a steady scale-free-like state. The closer the p$p$ approximates _pc$p_c$, the closer the scale-free-like distribution approximates the true scale-free distribution. Our results show that, in random network lack of growth, a preferential scheme does not necessarily lead to a scale-free state, and a formation of scale-free is a consequence of two mechanisms: (i) a preferential scheme and (ii) appropriate connection density.     

Experimental evidence of enhancement in the anticipation time by cascading

We have studied and verified experimentally the enhancement in the anticipation time by cascading Chua's circuits. The experiments have been carried out in a one dimensional array of Chua's circuits (2 to 8) coupled unidirectionally, such that each one acts as a master for the next one. By doing so, it has been observed that the anticipation time increases with an increase in the array size. Moreover, the numerical simulations of an array of eighty Chua's circuits verify the experimental observations.     

The emergence of coherent structures in coupled map lattices

We show how increasing spatial interaction leads to the merging of coherent structures from chaos in some systems of coupled map lattices. This phenomenon reflects the arising of new ground states in the corresponding model of statistical mechanics. If we further increase the coupling then, new ground states appear showing the coexistence of a large-scale coherent structure with a small-scale chaotic motion. This allows us to propose a generalization of the notion of spatial intermittency.     

Empirical analysis of a scale-free railway network in China

We present a detailed, empirical analysis of the statistical properties of the China Railway Network (CRN) consisting of 3915 nodes (train stations) and 22 259 edges (railways). Based on this, CRN displays two explicit features already observed in numerous real-world and artificial networks. One feature, the small-world property, has the fingerprint of a small characteristic shortest-path length, 3.5, accompanied by a high degree of clustering, 0.835. Another feature is characterized by the scale-free distributions of both degrees and weighted degrees, namely strengths. Correlations between strength and degree, degree and degree, and clustering coefficient and degree have been studied and the forms of such behaviors have been identified. In addition, we investigate distributions of clustering coefficients, topological distances, and spatial distances.     

Modeling and simulations of the cascading failure of multiple interdependent R&D networks under risk propagation

In this paper, we study the robustness of multiple interrelated R&D networks under risk propagation. Firstly, using a bi-partite graph to represent the interrelated R&D networks is emphasized and proposed. Secondly, a risk propagation model is built by defining risk load and risk capacity of each enterprise on a specific R&D network, Thirdly, we use simulations to study risk propagation in interrelated R&D networks. Our results indicate that there exist three critical thresholds to quantify the robustness of R&D networks. Risk propagation in R&D networks is highly affected by the heterogeneity of all enterprises' scales and risk capacities.