Congestion-gradient driven transport on complex networks

Abstract

We present a study of transport on complex networks with routing based on local information. Particles hop from one node of the network to another according to a set of routing rules with different degrees of congestion awareness, ranging from random diffusion to rigid congestion-gradient driven flow. Each node can be either source or destination for particles and all nodes have the same routing capacity, which are features of ad-hoc wireless networks. It is shown that the transport capacity increases when a small amount of congestion awareness is present in the routing rules, and that it then decreases as the routing rules become too rigid when the flow becomes strictly congestion-gradient driven. Therefore, an optimum value of the congestion awareness exists in the routing rules. It is also shown that, in the limit of a large number of nodes, networks using routing based on local information jam at any nonzero load. Finally, we study the correlation between congestion at node level and a betweenness centrality measure.

11 pages, 8 figures

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• Local Empathy provides Global Minimization of Congestion in Communication Networks
• Optimal transport on wireless networks
• Distributed flow optimization and cascading effects in weighted complex networks
• Partition of Networks into Basins of Attraction
• The Gradient Mechanism in a Communication Network
• Enhancing Transport Efficiency by Hybrid Routing Strategy
• Preferential Detachment in Broadcast Signaling Networks: Connectivity and Cost Trade-off
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• Detecting Structure of Complex Network by Quantum Bosonic Dynamics
• Traffic properties for stochastic routings on scale-free networks
• Adaptive Fractal-like Network Structure for Efficient Search of Inhomogeneously Distributed Targets at Unknown Positions