Introduction

In previous parts of this book, we have presented a wide range of techniques for analyzing social networks. We have discovered that one structural concept can often be measured in several ways (e.g., centrality). We have not encountered the reverse, that is, a single technique that is able to detect different kinds of structures (e.g., cohesion and centrality). In this final chapter, we present such a technique, which is called blockmodeling.

Blockmodeling is a flexible method for analyzing social networks. Several network concepts are sensitive to exceptions; for instance, a single arc may turn a ranking into a rankless cluster (Chapter 10). Empirical data are seldom perfect, so we need a tool for checking the structural features of a social network that allows for exceptions or error. Blockmodeling and hierarchical clustering, which are closely related, are such tools.

Although blockmodeling is a technique capable of detecting cohesion, core-periphery structures, and ranking, it does not replace the techniques presented in previous chapters. At present, blockmodeling is feasible and effective only for small dense networks, whereas the other techniques work better on large or sparse networks. In addition, blockmodeling is grounded on different structural concepts: equivalence and positions, which are related to the theoretical concepts of social role and role sets. Blockmodels group vertices into clusters and determine the relations between these clusters (e.g., one cluster is the center and another is the periphery). In contrast, the techniques discussed in previous chapters, such as the measures of centrality, compute the structural position of each vertex individually.