This study shows various ways that formal graph theoretic statements map patterns of network ties into substantive hypotheses about social cohesion. If network cohesion is enhanced by multiple connections between members of a group, for example, then the higher the global minimum of the number of independent paths that connect every pair of nodes in the network, the higher the social cohesion. The cohesiveness of a group is also measured by the extent to which it is not disconnected by removal of 1, 2, 3,..., n actors. Menger's Theorem proves that these two measures are equivalent. Within this graph theoretic framework, we evaluate the family of concepts of cohesion and establish the validity of a pair of related measures: 1. Connectivity - the minimum number k of its actors whose removal would not allow the group to remain connected or would reduce the group to but a single member - measures the social cohesion of a group at a general level. 2. Conditional density measures cohesion on a finer scale as a proportion of ties beyond that required by a graph's connectivity k over the number of ties that would force it to k + 1. Calibrated for successive values of k, these two measures combine into an aggregate measure of social cohesion, suitable for both small-and large-scale network studies. Using these measures to define the core of a new methodology of cohesive blocking, we offer hypotheses about the consequences of cohesive blocks for social groups and their members, and explore empirical examples that illustrate the significance, theoretical relevance, and predictiveness of cohesive blocking in a variety of substantively important applications in sociology.

In this article, we advance a methodology for capturing and tracing the emergence, evolution, and diffusion of a practice, conceptual understanding, resource, or student-constructed artifact. The Constructing Networks of Action-Relevant Episodes (CN–ARE) methodology allows researchers to identify relevant data from a complex, evolving environment, and then to organize it into a web of action that can illuminate the historical development (evolving trajectory) of the phenomenon of interest (e.g., conception of an eclipse, applications of a mathematical formula, an evolving student-constructed Website). To accomplish this end, experiences are (a) sectioned into action-relevant episodes (AREs), (b) parsed down to codes in a database, and (c) then represented as nodes in a network so that the historical development of the particular phenomenon of interest can be traced. The CN–ARE methodology is especially useful for researchers interested in carrying out design experiments in which research findings with respect to one iteration of a course are cycled into the design of future course instantiations. In addition to setting the context and providing a theoretical rationale for the CN–ARE methodology, this discussion includes an in-depth description of the methodology along with its application to data sets. Following these examples, we close with a discussion of the scope and limitations of this methodology, touching on issues of trustworthiness, credibility, and usefulness. Correspondence and requests for reprints should be sent to Sasha A. Barab, School of Education,

This paper identifies an important paradigm shift in social research on HIV transmission, drug abuse, and risk reduction research. The article describes the key research trends and the institutional support for social network analysis in the HIV and drug risk field for the past decade. Key hypotheses and recommended areas for future research are identified.

for stimulating discussions; and an anonymous reviewer for comments that led to clarifications.

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University for helpful comments on earlier drafts. THE DARK SIDE OF NETWORKS: AGE, STABILITY AND

The informal organizational chart in organizations: An approach from the social network analysis