Effects of link-orientation methods on robustness against cascading failures in complex networks

ZY Jiang, JF Ma, YL Shen, Y Zeng - Physica A: Statistical Mechanics and its …, 2016 - Elsevier
ZY Jiang, JF Ma, YL Shen, Y Zeng
Physica A: Statistical Mechanics and its Applications, 2016Elsevier
Unidirectional and bidirectional links may coexist in many realistic networked complex
systems such as the city transportation networks. Even more, for some considerations,
several bidirectional links are shifted to unidirectional ones. Many link-orientation strategies
might be employed, including High-to-Low, Low-to-High and Random direction-determining
methods, abbreviated as HTLDD, LTHDD and RDD respectively. Traffic passing through a
unidirectional link is restricted to one-side direction. In real complex systems, nodes are …
Abstract
Unidirectional and bidirectional links may coexist in many realistic networked complex systems such as the city transportation networks. Even more, for some considerations, several bidirectional links are shifted to unidirectional ones. Many link-orientation strategies might be employed, including High-to-Low, Low-to-High and Random direction-determining methods, abbreviated as HTLDD, LTHDD and RDD respectively. Traffic passing through a unidirectional link is restricted to one-side direction. In real complex systems, nodes are correlated with each other. The failure from an initial node may be propagated iteratively, resulting in a large scale of failures of other nodes, called cascade phenomenon which may damage the safety or security of the networked system. Assuming that traffic load on any failed node can be redistributed to its non-failed neighbors, in this work, we try to reveal the effects of unidirectional links on network robustness against cascades. Extensive simulations have been implemented on kinds of networks including Scale-Free networks, Small-World networks, and Erdös–Rényi random networks. The results showed that all of the above three direction-determining methods decrease the robustness of the original networks against cascading failure. This work can help network designers and managers understand the robustness of network well and efficiently prevent the safety events.
Elsevier
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