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Sampling designs for recovering local and global characteristics of social networks

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  • Ebbes, Peter
  • Huang, Zan
  • Rangaswamy, Arvind

Abstract

The trajectories of social processes (e.g., peer pressure, imitation, and assimilation) that take place on social networks depend on the structure of those networks. Thus, to understand a social process or to predict the associated outcomes accurately, marketers would need good knowledge of the social network structure. However, many social networks of relevance to marketers are large, complex, or hidden, making it prohibitively expensive to map out an entire social network. Instead, marketers often need to work with a sample (i.e., a subgraph) of a social network. In this paper we evaluate the efficacy of nine different sampling methods for generating subgraphs that recover four structural characteristics of importance to marketers, namely, the distributions of degree, clustering coefficient, betweenness centrality, and closeness centrality, which are important for understanding how social network structure influences outcomes of processes that take place on the network.

Suggested Citation

  • Ebbes, Peter & Huang, Zan & Rangaswamy, Arvind, 2016. "Sampling designs for recovering local and global characteristics of social networks," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 578-599.
    • Handle: RePEc:eee:ijrema:v:33:y:2016:i:3:p:578-599
    DOI: 10.1016/j.ijresmar.2015.09.009
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    Cited by:

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    2. Stolz, Simon & Schlereth, Christian, 2021. "Predicting Tie Strength with Ego Network Structures," Journal of Interactive Marketing, Elsevier, vol. 54(C), pages 40-52.

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