Today, an academic friend of mine asked me about a series of
papers I wrote on the topic of "how many X's do you know" questions. These papers are about a special kind of indirect "network" data that do not have detailed information on individual edges in a network. Rather, it contains counts of the edges that connects an ego towards a number of specific subpopulations.
Relational data, such as records of citations, online communication and social contacts, contain interesting information regarding the mechanisms that drive the dynamics of such interactions under different contexts. Current technology allows detailed observation and recording of these interaction, creating both opportunities and challenges.
Aggregated relation data (ARD) are local summaries of these interactions, via aggregation. This has become a useful and common means of learning about hidden, hard-to-count and relatively small populations in the social network, also known as the
network scale-up method.
Our
papers since 2006 started with proposing better statistical models for such data. We further discussed data collection insights we came to realize during our research. We showed that via innovative statistical modeling, ARD can be used to estimate personal network sizes, the sizes and demographic profiles of hidden populations, and non-random mixing structures of the general social network. In particular, in
our 2015 paper, we proposed a model for ARD that is close to the latent space model by Hoff et al (2002) for full-network data. This would allow us to connect and possibly combine information from ARD with partially observed full network data.
