Analysis of overload-based cascading failure in multilayer spatial networks*

Abstract

Many complex networks in real life are embedded in space and most infrastructure networks are interdependent, such as the power system and the transport network. In this paper, we construct two cascading failure models on the multilayer spatial network. In our research, the distance l between nodes within the layer obeys the exponential distribution P(l) ∼ exp(–l/ζ), and the length r of dependency link between layers is defined according to node position. An entropy approach is applied to analyze the spatial network structure and reflect the difference degree between nodes. Two metrics, namely dynamic network size and dynamic network entropy, are proposed to evaluate the spatial network robustness and stability. During the cascading failure process, the spatial network evolution is analyzed, and the numbers of failure nodes caused by different reasons are also counted, respectively. Besides, we discuss the factors affecting network robustness. Simulations demonstrate that the larger the values of average degree 〈k〉, the stronger the network robustness. As the length r decreases, the network performs better. When the probability p is small, as ζ decreases, the network robustness becomes more reliable. When p is large, the network robustness manifests better performance as ζ increases. These results provide insight into enhancing the robustness, maintaining the stability, and adjusting the difference degree between nodes of the embedded spatiality systems.

Risk assessment on multimodal transport network based on quality function deployment

“…In terms of the spread of cascading risks, Zhang et al 34 considered the comprehensive impact of risk factors, such as overloading, and established a cascading failure model for complex network risks, and analyzed the spatial propagation process of risks. Tang et al 35 used the direct interdependence between the nodes in the transport network, and quantified the dynamic propagation process of the failure load in the network by constructing a time‐varying function equation.…”

Robustness of community networks against cascading failures with heterogeneous redistribution strategies

“…Cascading failures have attracted considerable attention due to their widespread occurrence in various realworld systems and their highly destructive effect on network robustness. [1][2][3][4][5] Triggered by the failure of a single node or subsystem, the load of the failing node spreads to other nodes of the system, which in turn further increases the possibility of system failures, resulting in a vicious cycle or snowball effect. Cascading failures can bring the whole service down in a short space of time, and it will gradually deteriorate.…”

“…[6][7][8][9] For example, the heterogeneity of nodes and communities in networks has an important impact on the whole cascading failure process, and node loadredistribution strategies based on node's influence are continuously proposed. [3,4,10] Therefore, the impact of network structure on cascading failure has been one of the hot topics in research of complex network dynamics.…”

Effects of heterogeneous adoption thresholds on contact-limited social contagions

“…[1][2][3][4][5][6][7][8][9] A real complex system can be abstracted as a network, and then complex properties of the system, such as structure, robustness, function, as well as dynamical processes that occur on networks, can be investigated. [10][11][12][13][14][15][16][17][18][19][20][21][22][23] According to different characteristics of spreading objects, the spreading dynamics on complex networks can be generally divided into two categories: one is the dynamics of biological contagions with independent infection probabilities of any two contacts, such as the epidemic spreading and the spreading of computer viruses, [24][25][26][27][28][29][30] the other is the dynamics of social contagions with social reinforcement effect, including rumor diffusion, information spreading, behavior spreading, innovation adoption, and so on. [31][32][33][34][35][36][37][38] It is generally believed that social contagion is the expansion and application of biological contagion, and it is the research basis of social-biological coupling contagion.…”