Title |
Self-Healing Networks: Redundancy and Structure
|
---|---|
Published in |
PLOS ONE, February 2014
|
DOI | 10.1371/journal.pone.0087986 |
Pubmed ID | |
Authors |
Walter Quattrociocchi, Guido Caldarelli, Antonio Scala |
Abstract |
We introduce the concept of self-healing in the field of complex networks modelling; in particular, self-healing capabilities are implemented through distributed communication protocols that exploit redundant links to recover the connectivity of the system. We then analyze the effect of the level of redundancy on the resilience to multiple failures; in particular, we measure the fraction of nodes still served for increasing levels of network damages. Finally, we study the effects of redundancy under different connectivity patterns-from planar grids, to small-world, up to scale-free networks-on healing performances. Small-world topologies show that introducing some long-range connections in planar grids greatly enhances the resilience to multiple failures with performances comparable to the case of the most resilient (and least realistic) scale-free structures. Obvious applications of self-healing are in the important field of infrastructural networks like gas, power, water, oil distribution systems. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Italy | 5 | 19% |
United States | 4 | 15% |
United Kingdom | 4 | 15% |
Ecuador | 1 | 4% |
Philippines | 1 | 4% |
Germany | 1 | 4% |
Mexico | 1 | 4% |
Spain | 1 | 4% |
Canada | 1 | 4% |
Other | 1 | 4% |
Unknown | 7 | 26% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 18 | 67% |
Scientists | 9 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Italy | 3 | 3% |
Switzerland | 2 | 2% |
Germany | 1 | <1% |
Brazil | 1 | <1% |
Mexico | 1 | <1% |
Unknown | 98 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 29 | 27% |
Student > Master | 21 | 20% |
Researcher | 16 | 15% |
Student > Bachelor | 7 | 7% |
Student > Doctoral Student | 7 | 7% |
Other | 14 | 13% |
Unknown | 12 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 24 | 23% |
Engineering | 23 | 22% |
Physics and Astronomy | 17 | 16% |
Social Sciences | 4 | 4% |
Agricultural and Biological Sciences | 3 | 3% |
Other | 16 | 15% |
Unknown | 19 | 18% |