Abstract:
Once network data on a group is collected, we often ask - what is the structure? That is, what are the subgroups and how do they interact? Within SNA we have a number of partitioning and clustering algorithms for locating groups. A key limitation of these approaches is that they partition the group into distinct nonoverlapping sets. A second limitation is that they do not take non-social network data into account when locating the groups. A third limitation is that, they often only pull out the surface structure of the group - that which is common to all - and don't provide guidance as to the deep structure. In this paper, using data on a large university department, these limitations are illustrated. Then we demonstrate how these limitations can be, at least partially overcome, using data on multiple networks - social and knowledge; segregating overly shared information, and using fuzzy set partitioning.