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Social distance and network structures

Author

Listed:
  • Iijima, Ryota

    (Department of Economics, Yale University)

  • Kamada, Yuichiro

    (Haas School of Business, University of California, Berkeley)

Abstract

This paper analyzes how agents' perception of relationships with others determines the structures of networks. In our model, agents are endowed with their own multi-dimensional characteristics and form links depending on the social distance between them. We characterize average path length and clustering coefficient in stable networks, and analyze how they are related to the way social distances are measured by agents. One implication is that the introduction of new communication technology makes a network closely connected but not cliquish. We relate our model and results to Granovetter's ``strength of weak ties hypothesis," Tversky's ``similarity scale," and Mobius-Rosenblat's ``communication externality."

Suggested Citation

  • Iijima, Ryota & Kamada, Yuichiro, 2017. "Social distance and network structures," Theoretical Economics, Econometric Society, vol. 12(2), May.
    • Handle: RePEc:the:publsh:1873
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    Citations

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    Cited by:

    1. Christos Mavridis & Nikolas Tsakas, 2021. "Social Capital, Communication Channels and Opinion Formation," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 56(4), pages 635-678, May.
    2. Melguizo Lopez, Isabel, 2019. "Group size and network formation," MPRA Paper 91428, University Library of Munich, Germany.
    3. Qiaowei Shen & J. Miguel Villas-Boas, 2018. "Behavior-Based Advertising," Management Science, INFORMS, vol. 64(5), pages 2047-2064, May.
    4. Isabel Melguizo, 2023. "Group representation concerns and network formation," Bulletin of Economic Research, Wiley Blackwell, vol. 75(1), pages 151-179, January.
    5. Hitomu Kotani & Muneta Yokomatsu, 2019. "Quantitative evaluation of the roles of community events and artifacts for social network formation: a multilayer network model of a community of practice," Computational and Mathematical Organization Theory, Springer, vol. 25(4), pages 428-463, December.
    6. Nica-Avram, Georgiana & Harvey, John & Smith, Gavin & Smith, Andrew & Goulding, James, 2021. "Identifying food insecurity in food sharing networks via machine learning," Journal of Business Research, Elsevier, vol. 131(C), pages 469-484.
    7. Jasmina Arifovic & Giuseppe Danese, 2018. "Homophily and Social Norms in Experimental Network Formation Games," Games, MDPI, vol. 9(4), pages 1-22, October.
    8. Linardi, Fernando & Diks, Cees & van der Leij, Marco & Lazier, Iuri, 2020. "Dynamic interbank network analysis using latent space models," Journal of Economic Dynamics and Control, Elsevier, vol. 112(C).
    9. Jiménez-Martínez, Antonio & Melguizo-López, Isabel, 2022. "Making friends: The role of assortative interests and capacity constraints," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 431-465.
    10. Ayoubi, Charles & Thurm, Boris, 2020. "Evolution and Heterogeneity of Social Preferences," OSF Preprints ucx8z, Center for Open Science.
    11. Pinar Yildirim & Yanhao Wei & Christophe Bulte & Joy Lu, 2020. "Social network design for inducing effort," Quantitative Marketing and Economics (QME), Springer, vol. 18(4), pages 381-417, December.
    12. Patrick Allmis, 2024. "Cohesion, Ideology, and Tolerance," Papers 2407.14045, arXiv.org.

    More about this item

    Keywords

    Network formation; heterogeneity; spatial type topologies; clustering; average path length; weak-ties;
    All these keywords.

    JEL classification:

    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • A14 - General Economics and Teaching - - General Economics - - - Sociology of Economics

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