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How Variability in Individual Patterns of Behavior Changes the Structural Properties of Networks

Author

Listed:
  • Somayeh Koohborfardhaghighi

    (Technology Management, Economics, and Policy Program, College of Engineering, Seoul National University)

  • Jorn Altmann

    (Technology Management, Economics, and Policy Program, College of Engineering, Seoul National University)

Abstract

Dynamic processes in complex networks have received much attention. This attention reflects the fact that dynamic processes are the main source of changes in the structural properties of complex networks (e.g., clustering coefficient and average shortest-path length). In this paper, we develop an agent-based model to capture, compare, and explain the structural changes within a growing social network with respect to individuals’ social characteristics (e.g., their activities for expanding social relations beyond their social circles). According to our simulation results, the probability increases that the network’s average shortest-path length is between 3 and 4, if most of the dynamic processes are based on random link formations. That means, in Facebook, the existing average shortest path length of 4.7 can even shrink to smaller values. Another result is that, if the node increase is larger than the link increase when the network is formed, the probability increases that the average shortest-path length is between 4 and 8.

Suggested Citation

  • Somayeh Koohborfardhaghighi & Jorn Altmann, 2014. "How Variability in Individual Patterns of Behavior Changes the Structural Properties of Networks," TEMEP Discussion Papers 2014114, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Jun 2014.
    • Handle: RePEc:snv:dp2009:2014114
    as

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    File URL: http://temep-repec.my-groups.de/DP-114.pdf
    File Function: First version, 2014
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    References listed on IDEAS

    as
    1. Jackson, Matthew O. & Wolinsky, Asher, 1996. "A Strategic Model of Social and Economic Networks," Journal of Economic Theory, Elsevier, vol. 71(1), pages 44-74, October.
    2. Andrea Galeotti & Sanjeev Goyal & Matthew O. Jackson & Fernando Vega-Redondo & Leeat Yariv, 2010. "Network Games," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 77(1), pages 218-244.
    3. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    4. Somayeh Koohborfardhaghighi & Jorn Altmann, 2014. "How Placing Limitations on the Size of Personal Networks Changes the Structural Properties of Complex Networks," TEMEP Discussion Papers 2014110, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Jan 2014.
    5. Somayeh Koohborfardhaghighi & Jorn Altmann, 2014. "How Structural Changes in Complex Networks Impact Organizational Learning Performance," TEMEP Discussion Papers 2014111, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Mar 2014.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Somayeh Koohborfardhaghighi & Jorn Altmann, 2016. "How Network Visibility and Strategic Networking Leads to the Emergence of Certain Network Characteristics: A Complex Adaptive System Approach," TEMEP Discussion Papers 2016130, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Aug 2016.
    2. Somayeh Koohborfardhaghighi & Jorn Altmann, 2016. "How Strategic Networking Impacts the Networking Outcome: A Complex Adaptive System Approach," TEMEP Discussion Papers 2016131, Seoul National University; Technology Management, Economics, and Policy Program (TEMEP), revised Aug 2016.

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    More about this item

    Keywords

    Network Properties; Network Growth Models; Small World Theory; Network Science; Simulation; Clustering Coefficient; Complex Networks.;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation

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