Efficient routing on complex networks

In this letter, we propose a new routing strategy to improve the transportation efficiency on complex networks. Instead of using the routing strategy for shortest path, we give a generalized routing algorithm to find the so-called {\it efficient path}, which considers the possible congestion in the nodes along actual paths. Since the nodes with largest degree are very susceptible to traffic congestion, an effective way to improve traffic and control congestion, as our new strategy, can be as redistributing traffic load in central nodes to other non-central nodes. Simulation results indicate that the network capability in processing traffic is improved more than 10 times by optimizing the efficient path, which is in good agreement with the analysis.Comment: 4 pages, 4 figure

Traffic congestion in interconnected complex networks

Traffic congestion in isolated complex networks has been investigated extensively over the last decade. Coupled network models have recently been developed to facilitate further understanding of real complex systems. Analysis of traffic congestion in coupled complex networks, however, is still relatively unexplored. In this paper, we try to explore the effect of interconnections on traffic congestion in interconnected BA scale-free networks. We find 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. We analyze interconnected Internet AS-level graphs of South Korea and Japan and obtain similar results. Some practical suggestions are presented to optimize such real-world interconnected networks accordingly.Comment: 8 page

Integrating static and dynamic information for routing traffic

The efficiency of traffic routing on complex networks can be reflected by two key measurements i.e. the system capacity and the average data packets travel time. In this paper, we propose a mixing routing strategy by integrating local static and dynamic information for enhancing the efficiency of traffic on scale-free networks. The strategy is governed by a single parameter. Simulation results show that there exists a optimal parameter value by considering both maximizing the network capacity and reducing the packet travel time. Comparing with the strategy by adopting exclusive local static information, the new strategy shows its advantages in improving the efficiency of the system. The detailed analysis of the mixing strategy is provided. This work suggests that how to effectively utilize the larger degree nodes plays the key role in the scale-free traffic systems.Comment: 5 pages, 5 figure

The effect of bandwidth in scale-free network traffic

We model information traffic on scale-free networks by introducing the bandwidth as the delivering ability of links. We focus on the effects of bandwidth on the packet delivering ability of the traffic system to better understand traffic dynamic in real network systems. Such ability can be measured by a phase transition from free flow to congestion. Two cases of node capacity C are considered, i.e., C=constant and C is proportional to the node's degree. We figured out the decrease of the handling ability of the system together with the movement of the optimal local routing coefficient $\alpha_c$, induced by the restriction of bandwidth. Interestingly, for low bandwidth, the same optimal value of $\alpha_c$ emerges for both cases of node capacity. We investigate the number of packets of each node in the free flow state and provide analytical explanations for the optimal value of $\alpha_c$. Average packets traveling time is also studied. Our study may be useful for evaluating the overall efficiency of networked traffic systems, and for allevating traffic jam in such systems.Comment: 6 pages, 4 figure