2nd International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems

Research Article

Toward Bio-Inspired Network Robustness - Step 1. Modularity

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  • @INPROCEEDINGS{10.4108/ICST.BIONETICS2007.2437,
        author={Suyong Eum and Shinichi Arakawa and Masayuki Murata},
        title={Toward Bio-Inspired Network Robustness - Step 1. Modularity},
        proceedings={2nd International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems},
        proceedings_a={BIONETICS},
        year={2008},
        month={8},
        keywords={Modularity  Random and intentional attack  Robustness  cascading failure  traffic dynamic},
        doi={10.4108/ICST.BIONETICS2007.2437}
    }
    
  • Suyong Eum
    Shinichi Arakawa
    Masayuki Murata
    Year: 2008
    Toward Bio-Inspired Network Robustness - Step 1. Modularity
    BIONETICS
    ICST
    DOI: 10.4108/ICST.BIONETICS2007.2437
Suyong Eum1,*, Shinichi Arakawa1,*, Masayuki Murata1,*
  • 1: Osaka University, Graduate School of Information Science and Technology 1-5 Yamadaoka, Suita, Osaka, 565-0871 Japan
*Contact email: suyong@ist.osaka-u.ac.jp, arakawa@ist.osaka-u.ac.jp, murata@ist.osaka-u.ac.jp

Abstract

Biological systems have evolved themselves to withstand against perturbations so that a characteristic, called robustness, is the most commonly observed feature in all living organisms. To find out the secret of robustness in biological systems, many researchers have investigated the system level structure of biological organizations. One of the known structural features that enable biological systems to be robust is modularity. In this paper we study the correlation between modularity structure and robustness in IP networks. We carry out a simulation study to observe resistibility of different topologies, which have different level of modularity structure, against a perturbation created synthetically. The numerical results show that the quantified modularity seems to be more important measure to understand robustness of IP networks than any other common properties such as clustering coefficient, degree distribution, and average path length.