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The Impact of the Subgroup Structure on the Evolution of Networks: An Economic Model of Network Evolution

Author

Listed:
  • Kibae Kim

    ()

  • Jorn Altmann

    ()

  • Junseok Hwang

    (Technology Management, Economics, and Policy Program (TEMEP), Seoul National University)

Abstract

One of the most important properties of self-organized networks is their scale-free property. Prior research proved empirically and theoretically that scale-free networks emerge under the preferential attachment rule. However, a few empirical studies also show that empirical networks diverge from the structure of scale-free networks. Empirical networks exhibit a lower exponent of the power law distribution than constructed scale-free networks. Our research aims at establishing a simple evolutionary network model that explains this difference. The results of our model suggest that there are two reasons for this discrepancy. First, as already known, additional links between existing nodes distort the scale-free feature. Second, boundaries between subgroups (groups of network nodes) distort the degree distribution. In general, we believe that our evolutionary model may be applicable not only to describe the structural evolution of networks but also to make network design recommendations in a variety of areas such as WWW-hyperlink networks, business collaboration networks, Peer-To-Peer Networks, and Web2.0 service networks.

Suggested Citation

  • Kibae Kim & Jorn Altmann & Junseok Hwang, 2010. "The Impact of the Subgroup Structure on the Evolution of Networks: An Economic Model of Network Evolution," TEMEP Discussion Papers 201056, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Feb 2010.
  • Handle: RePEc:snv:dp2009:201056
    as

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    File URL: ftp://147.46.237.98/DP-56.pdf
    File Function: First version, 2010
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    References listed on IDEAS

    as
    1. Fu, Feng & Liu, Lianghuan & Wang, Long, 2008. "Empirical analysis of online social networks in the age of Web 2.0," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(2), pages 675-684.
    2. Galeotti, Andrea & Goyal, Sanjeev & Kamphorst, Jurjen, 2006. "Network formation with heterogeneous players," Games and Economic Behavior, Elsevier, vol. 54(2), pages 353-372, February.
    3. Junseok Hwang & Jorn Altmann & Kibae Kim, 2009. "The Structural Evolution of the Web2.0 Service Network," TEMEP Discussion Papers 200914, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Sep 2009.
    4. Wagner, Caroline S. & Leydesdorff, Loet, 2005. "Network structure, self-organization, and the growth of international collaboration in science," Research Policy, Elsevier, vol. 34(10), pages 1608-1618, December.
    5. Galeotti, Andrea & Goyal, Sanjeev & Kamphorst, Jurjen, 2006. "Network formation with heterogeneous players," Games and Economic Behavior, Elsevier, vol. 54(2), pages 353-372, February.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Kibae Kim & Jörn Altmann, 2015. "Effect of Homophily on Network Evolution," TEMEP Discussion Papers 2015121, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Mar 2017.
    2. Kibae Kim & Jorn Altmann & Junseok Hwang, 2010. "Measuring and Analyzing the Openness of the Web2.0 Service Network for Improving the Innovation Capacity of the Web2.0 System through Collective Intelligence," TEMEP Discussion Papers 201057, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Mar 2010.
    3. Kibae Kim & Jorn Altmann, 2013. "Evolution of the Software-as-a-Service Innovation System Through Collective Intelligence," TEMEP Discussion Papers 2013108, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Dec 2013.
    4. Kibae Kim & Jorn Altmann & Junseok Hwang, 2010. "An Analysis of the Openness of the Web2.0 Service Network Based on Two Sets of Indices for Measuring the Impact of Service Ownership," TEMEP Discussion Papers 201067, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Oct 2010.
    5. Kibae Kim & Wool-rim Lee & Jorn Altmann, 2014. "SNA-Based Innovation Trend Analysis in Software Service Networks," TEMEP Discussion Papers 2014115, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Aug 2014.

    More about this item

    Keywords

    Social network analysis; scale-free networks; self-organization; evolutionary model; network design; network science; network modeling.;

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools
    • D02 - Microeconomics - - General - - - Institutions: Design, Formation, Operations, and Impact
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management
    • M21 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics - - - Business Economics

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