Traffic Congestion And Cascades On Complex Networks

Over the last15years, as the new science of networks complex networks have been extensively applied in various fields of science and engineering, and provide a novel perspective for us to understand and design complex networked systems in real world. Thus, complex networks have attracted more and more attention from researchers. In this thesis, we investigate traffic congestion and cascading failures as two key dynamical processes on complex networks. These two studies have great roles both in theory and in practical applications. Through analyzing different factors accounting for traffic congestion in complex networks and studying corresponding routing strategies, on one hand, we can have deeper and more comprehensive understanding of how the interplay of different elements affect traffic congestion in networks; on the other hand, these theoretical results can be applied to guide us to adopt more effective routing protocols and design and optimize the coupling mechanisms of real-life networks. Besides, the analysis of cascading failures in interconnected and interdependent networks as two representative types of coupled networks, on one hand, expands our understanding of coupled networks, especially the robustness of interdependent networks. On the other hand, we can apply these new insights to optimize and design more robust coupled networks. To be concrete, the main contents and results of the thesis are summarized as follows:1. We propose hybrid routing protocol on scale-free networks, and it combines both static structure properties and dynamical traffic information. Our results show that hybrid routing overwhelms other two recently proposed routing algorithms by providing larger network traffic capacity and shorter average traveling time.2. We analyze traffic congestion in interconnected networks. Our results show that assortative coupling can alleviate traffic congestion more readily than disassortative and random coupling when the node processing capacity is allocated based on node usage probability. Furthermore, the optimal coupling probability can be found for assortative coupling. However, three types of coupling preferences achieve similar traffic performance if all nodes share the same processing capacity.3. We analyze cascading failures in interconnected networks when traffic overload is taken into account. Our results show that the robustness of the assortative coupling is better than that of disassortative and random coupling. Furthermore, the corresponding optimal coupling probability can be found for three coupling patterns.4. We study cascading failures in interdependent networks when traffic overload is taken into account. Our results show that the dependency will reduce the robustness of networks, but the extent is largely subjected to the coupling pattern. To be concrete, interdependent ER random networks are robust-yet-fragile under both intentional attacks and random failures. In addition, interdependent BA scale-free networks are also robust-yet-fragile under random failures, whereas they are fragile under intentional attacks. This work can help us to refresh our impression on the robustness of interdependent networks.