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Modeling social networks from sampled data. (English) Zbl 1189.62187

Summary: Network models are widely used to represent relational information among interacting units and the structural implications of these relations. Recently, social network studies have focused a great deal of attention on random graph models of networks whose nodes represent individual social actors and whose edges represent a specified relationship between the actors. Most inference for social network models assumes that the presence or absence of all possible links is observed, that the information is completely reliable, and that there are no measurement (e.g., recording) errors. This is clearly not true in practice, as much network data is collected though sample surveys. In addition, even if a census of a population is attempted, individuals and links between individuals are missed (i.e., do not appear in the recorded data).
We develop the conceptual and computational theory for inference based on sampled network information. We first review forms of network sampling designs used in practice. We consider inference from the likelihood framework, and develop a typology of network data that reflects their treatment within this frame. We then develop inference for social network models based on information from adaptive network designs. We motivate and illustrate these ideas by analyzing the effect of link-tracing sampling designs on a collaboration network.

MSC:

62P25 Applications of statistics to social sciences
91D30 Social networks; opinion dynamics
05C80 Random graphs (graph-theoretic aspects)
62D05 Sampling theory, sample surveys
65C40 Numerical analysis or methods applied to Markov chains
05C90 Applications of graph theory

Software:

R; statnet
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References:

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