Literature DB >> 26651743

Simple and efficient self-healing strategy for damaged complex networks.

Lazaros K Gallos1, Nina H Fefferman1.   

Abstract

The process of destroying a complex network through node removal has been the subject of extensive interest and research. Node loss typically leaves the network disintegrated into many small and isolated clusters. Here we show that these clusters typically remain close to each other and we suggest a simple algorithm that is able to reverse the inflicted damage by restoring the network's functionality. After damage, each node decides independently whether to create a new link depending on the fraction of neighbors it has lost. In addition to relying only on local information, where nodes do not need knowledge of the global network status, we impose the additional constraint that new links should be as short as possible (i.e., that the new edge completes a shortest possible new cycle). We demonstrate that this self-healing method operates very efficiently, both in model and real networks. For example, after removing the most connected airports in the USA, the self-healing algorithm rejoined almost 90% of the surviving airports.

Year:  2015        PMID: 26651743     DOI: 10.1103/PhysRevE.92.052806

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  4 in total

1.  More Tolerant Reconstructed Networks Using Self-Healing against Attacks in Saving Resource.

Authors:  Yukio Hayashi; Atsushi Tanaka; Jun Matsukubo
Journal:  Entropy (Basel)       Date:  2021-01-12       Impact factor: 2.524

2.  Onion-like networks are both robust and resilient.

Authors:  Yukio Hayashi; Naoya Uchiyama
Journal:  Sci Rep       Date:  2018-07-26       Impact factor: 4.379

Review 3.  The role of social structure and dynamics in the maintenance of endemic disease.

Authors:  Matthew J Silk; Nina H Fefferman
Journal:  Behav Ecol Sociobiol       Date:  2021-08-18       Impact factor: 2.980

4.  Localized recovery of complex networks against failure.

Authors:  Yilun Shang
Journal:  Sci Rep       Date:  2016-07-26       Impact factor: 4.379

  4 in total

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