Robustness Of Interconnected Networks Research Articles

Gray-information-based robustness of interdependent networks under attack on edges

The researches on robustness of interconnected networks have received more and more attention. However, previous studies of robustness are based on two extreme attack strategies, i.e. random attack and targeted attack. In the real world, the attack information is usually ambiguous. In this paper, the gray-information-based attack model is adopted to evaluate the robustness of the interdependent networks against the attack on edges. Through extensive simulation, we find that the robustness of the interdependent networks under gray-information-based attack gets stronger with the increase of redundancy parameter β. We also get the conclusion that the robustness of the interdependent network in the scenarios of gray-information-based attack is stronger than that of targeted attack and weaker than that of random attack. Finally, by comparison of three types of coupled networks, NW-NW and WS-WS coupled network are found to be the most robust type under gray-information attack.

Cascade-robustness optimization of coupling preference in interconnected networks

Recently, the robustness of interconnected networks has attracted extensive attentions, one of which is to investigate the influence of coupling preference. In this paper, the memetic algorithm (MA) is employed to optimize the coupling links of interconnected networks. Afterwards, a comparison is made between MA optimized coupling strategy and traditional assortative, disassortative and random coupling preferences. It is found that the MA optimized coupling strategy with a moderate assortative value shows an outstanding performance against cascading failures on both synthetic scale-free interconnected networks and real-world networks. We then provide an explanation for this phenomenon from a micro-scope point of view and propose a coupling coefficient index to quantify the coupling preference. Our work is helpful for the design of robust interconnected networks.

Fuzzy-information-based robustness of interconnected networks against attacks and failures

Cascading failure is fatal in applications and its investigation is essential and therefore became a focal topic in the field of complex networks in the last decade. In this paper, a cascading failure model is established for interconnected networks and the associated data-packet transport problem is discussed. A distinguished feature of the new model is its utilization of fuzzy information in resisting uncertain failures and malicious attacks. We numerically find that the giant component of the network after failures increases with tolerance parameter for any coupling preference and attacking ambiguity. Moreover, considering the effect of the coupling probability on the robustness of the networks, we find that the robustness of the assortative coupling and random coupling of the network model increases with the coupling probability. However, for disassortative coupling, there exists a critical phenomenon for coupling probability. In addition, a critical value that attacking information accuracy affects the network robustness is observed. Finally, as a practical example, the interconnected AS-level Internet in South Korea and Japan is analyzed. The actual data validates the theoretical model and analytic results. This paper thus provides some guidelines for preventing cascading failures in the design of architecture and optimization of real-world interconnected networks.

The effect of capacity redundancy disparity on the robustness of interconnected networks

Cascading failures in interconnected networks have received more and more attention. In previous works, the basic assumption is that networks share the same capacity redundancy. However, this setting cannot capture the real case very well. Hence, in this paper, we analyze the effect of capacity redundancy disparity on the robustness of interconnected networks. In isolated networks, it is well known that the complex network’s robustness can be improved by increasing its capacity redundancy. In interconnected networks where two networks share the same capacity redundancy, the similar result holds. Yet this result is not necessarily true in interconnected networks where two networks are different in capacity redundancy. We find that when the capacity redundancy of one network is fixed, the robustness of the whole system may not follow another network’s capacity redundancy. More specifically, when the fixed network’s capacity redundancy is very small or very large, the robustness of the whole system increases as another network’s capacity redundancy increases. But there exists a certain range within which the increase of one network’s capacity redundancy results in the robustness decline of the whole system. This counterintuitive feature appears under different coupling patterns such as assortative, disassortative, or random coupling. This result advances our understanding of the robustness of interconnected networks.

Effect of network size on robustness of interconnected networks under targeted attack

Cascading failures in coupled networks have been intensively studied recently. The underlying assumption is that all networks share the same size. However, this setting cannot capture real-world coupled networks very well. Hence, in this paper, we explore how different network sizes affect cascades from the perspective of the whole interconnected networks. We find that when two networks with similar sizes are coupled, the interconnected networks are the most fragile for sparse coupling while they are the most robust for dense coupling. Moreover, we also study the effect of network size on the robustness of individual network. We find that when two networks with different sizes are coupled, the larger one is more robust for sparse coupling, while more fragile for dense coupling.

Cascading failures in interconnected networks with dynamical redistribution of loads

Cascading failures of loads in isolated networks and coupled networks have been studied in the past few years. In most of the corresponding results, the topologies of the networks are destroyed. Here, we present an interconnected network model considering cascading failures based on the dynamic redistribution of flow in the networks. Compared with the results of single scale-free networks, we find that interconnected scale-free networks have higher vulnerability. Additionally, the network heterogeneity plays an important role in the robustness of interconnected networks under intentional attacks. Considering the effects of various coupling preferences, the results show that there are almost no differences. Finally, the application of our model to the Beijing interconnected traffic network, which consists of a subway network and a bus network, shows that the subway network suffers more damage under the attack. Moreover, the interconnected traffic network may be more exposed to damage after initial attacks on the bus network. These discussions are important for the design and optimization of interconnected networks.