Abstract
A common goal when analyzing a social network is to try and determine the cohesive subgroup structure of the network. Some techniques result in complex overlapping structures such as cliques or k-plexes whereas others either partition the network or place actors into unique groups, for example factions or components. A standard method implemented in Ucinet uses the overlapping structure to construct a proximity matrix which can be submitted to a clustering routine to find non-overlapping groups. Once the groups have been determined many analysts relate these to observations they have made involving their data. There are few techniques that take the groups as a starting point for additional analysis with the exception of Krachardt's work on Simelian ties. In this paper I examine the roles the actors play in the cohesive structure using the brokerage ideas of Gould and Fernandez. The method is demonstrated on an original dataset that was collected in 2006 on 100 social services based in a suburb area of Milan, Italy. The dataset is composed of a matrix that contains directed data collected by asking the spokesperson of every social service if he/she knows the other services listed (the list of services having been previously compiled using several sources), together with attribute information. The matrix is analyzed using the clique analysis techniques in the Ucinet software package to find non-overlapping groups. The results are submitted to Ucinet's brokerage routine and visualized using the Netdraw software package. This analysis offers a new insight into the data, as it is possible to identify what actors play important roles (and which kind of role) in bridging the gaps between the cohesive subgroups.