17 - Future Directions

Summary

We conclude this book by speculating a bit about the future of social network methodology. The following comments include observations about gaps in current network methods and “hot” trends that we think are likely to continue. We also include some wishful thinking about the directions in which we would like to see network methodology develop.

Statistical Models

We believe that statistical models will be a major focus for continued development and expansion of network methods. Clearly scientific understanding is advanced when we can test propositions about network properties rather than simply relying on descriptive statements. Great steps have been made in statistical models for dyads (including p₁ and its relatives for valued relations, multiple relations, and for networks including actor attributes). We expect that further development of Markov graph models, logistic regressions, and so on will make statistical models more useful. Such models avoid the assumptions of dyadic independence, and thus promise to be more “realistic” than models of social networks that assume dyadic independence.

These future developments make use of very important research by Frank and Strauss (1986) and Strauss and Ikeda (1990) on Markov random graphs. Specifically, one can postulate statistical models for social networks which do not assume dyads are independent; in fact, the dependence structure of these models can be quite complicated. However, fitting them exactly is quite tedious computationally, unless one relies on the approximations described by Strauss and Ikeda, which allow one to calculate approximate maximum likelihood estimates of model parameters using logistic regression. These models, because of their generality and realism, have tremendous potential, which has yet to be realized.