Inducing a blockmodel structure of two-mode binary data using seriation procedures

We show that seriation of the rows and columns of a two-mode, binary matrix can be an effective method for producing a reordering of the matrix that reveals a blockmodel structure of the data. The objective criterion of the seriation process is based on Robinson patterning of matrix elements. The key advantages of the proposed method are: (a) it can be used in conjunction with existing two-mode blockmodeling algorithms by facilitating selection of the number of classes for the rows and columns of the matrix and the appropriate types of ideal blocks; (b) the model uses a well-grounded index based on Robinson structure, (c) guaranteed optimal solutions can be obtained for problems of practical size, and (d) the seriation method is frequently capable of producing a solution that has a substantive interpretation with respect to the orderings of the row objects and column items.     

A variable neighborhood search method for generalized blockmodeling of two-mode binary matrices

The clustering of two-mode proximity matrices is a challenging combinatorial optimization problem that has important applications in the quantitative social sciences. We focus on one particular type of problem related to the clustering of a two-mode binary matrix, which is relevant to the establishment of generalized blockmodels for social networks. In this context, clusters for the rows of the two-mode matrix intersect with clusters of the columns to form blocks, which should ideally be either complete (all 1s) or null (all 0s). A new procedure based on variable neighborhood search is presented and compared to an existing two-mode K-means clustering algorithm. The new procedure generally provided slightly greater explained variation; however, both methods yielded exceptional recovery of cluster structure.