To date, most network research contains one or more of five major
problems. First, it tends to be atheoretical, ignoring the various social
theories that contain network implications. Second, it explores single levels of
analysis rather than the multiple levels out of which most networks are
comprised. Third, network analysis has employed very little the insights from
contemporary complex systems analysis and computer simulations. Foruth, it
typically uses descriptive rather than inferential statistics, thus robbing it
of the ability to make claims about the larger universe of networks. Finally,
almost all the research is static and cross-sectional rather than dynamic.
Theories of Communication Networks presents solutions to all five
problems. The authors develop a multitheoretical model that relates different
social science theories with different network properties. This model is
multilevel, providing a network decomposition that applies the various social
theories to all network levels: individuals, dyads, triples, groups, and the
entire network. The book then establishes a model from the perspective of
complex adaptive systems and demonstrates how to use Blanche, an agent-based
network computer simulation environment, to generate and test network theories
and hypotheses. It presents recent developments in network statistical anlysis,
the p* family, which provides a basis for valid multilevel statistical
inferences regarding networks. Finally, it shows how to relate communication
networks to other networks, thus providing the basis in conjunction with
computer simulations to study the emergence of dynamic organizational
networks
