Bio-Inspired Models of Network, Information, and Computing Systems. 5th International ICST Conference, BIONETICS 2010, Boston, USA, December 1-3, 2010, Revised Selected Papers

Research Article

Reconstructing History of Social Network Evolution Using Web Search Engines

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  • @INPROCEEDINGS{10.1007/978-3-642-32615-8_18,
        author={Jin Akaishi and Hiroki Sayama and Shelley Dionne and Xiujian Chen and Alka Gupta and Chanyu Hao and Andra Serban and Benjamin Bush and Hadassah Head and Francis Yammarino},
        title={Reconstructing History of Social Network Evolution Using Web Search Engines},
        proceedings={Bio-Inspired Models of Network, Information, and Computing Systems. 5th International ICST Conference, BIONETICS 2010, Boston, USA, December 1-3, 2010, Revised Selected Papers},
        proceedings_a={BIONETICS},
        year={2012},
        month={10},
        keywords={Social networks adaptive networks network evolution centrality data collection web search engines},
        doi={10.1007/978-3-642-32615-8_18}
    }
    
  • Jin Akaishi
    Hiroki Sayama
    Shelley Dionne
    Xiujian Chen
    Alka Gupta
    Chanyu Hao
    Andra Serban
    Benjamin Bush
    Hadassah Head
    Francis Yammarino
    Year: 2012
    Reconstructing History of Social Network Evolution Using Web Search Engines
    BIONETICS
    Springer
    DOI: 10.1007/978-3-642-32615-8_18
Jin Akaishi,*, Hiroki Sayama,*, Shelley Dionne,*, Xiujian Chen,*, Alka Gupta,*, Chanyu Hao,*, Andra Serban,*, Benjamin Bush,*, Hadassah Head,*, Francis Yammarino,*
    *Contact email: jakaishi@binghamton.edu, sayama@binghamton.edu, sdionne@binghamton.edu, xichen@binghamton.edu, agupta1@binghamton.edu, chao2@binghamton.edu, aserban1@binghamton.edu, bbush2@binghamton.edu, hhead1@binghamton.edu, fjyammo@binghamton.edu

    Abstract

    We propose a simple web search engine based method for collecting approximated historical data of temporally changing social adaptive networks, which are rather difficult to obtain experimentally in conventional research methods. In the proposed method, a search query string is combined with additional keywords that specify inclusion/exclusion of specific years to limit the search results to a particular time point. Using the proposed method, we reconstructed the temporal evolution of a social network from 2005 to 2009 of 93 individuals who are important in the US economy. We measured centralities of those individuals for every year and found several illustrative cases where the temporal change of centrality of an individual correctly captured the actual events that are related to him/her over this time period. These results indicate the effectiveness of the proposed method. Limitations and future directions of research are discussed.