Optimization of network structure to random failures

Abstract

Network's resilience to the malfunction of its components has been of great concern. The goal of this work is to determine the network design guidelines, which maximizes the network efficiency while keeping the cost of the network (that is the average connectivity) constant. With a global optimization method, memory tabu search (MTS), we get the optimal network structure with the approximately best efficiency. We analyze the statistical characters of the network and find that a network with a small quantity of hub nodes, high degree of clustering may be much more resilient to perturbations than a random network and the optimal network is one kind of highly heterogeneous networks. The results strongly suggest that networks with higher efficiency are more robust to random failures. In addition, we propose a simple model to describe the statistical properties of the optimal network and investigate the synchronizability of this model.

11 pages, 6 figures, accepted by Physica A

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Cited by in corpus (6)

• A High Robustness and Low Cost Model for Cascading Failures
• Relations between Average Distance, Heterogeneity and Network Synchronizability
• A limited resource model of fault-tolerant capability against cascading failure of complex network
• Efficient Rewirings for Enhancing Synchronizability of Dynamical Networks
• Growing Scale-free Small-world Networks with Tunable Assortative Coefficient
• Enhancing synchronization in growing networks