Blockmodeling

Blockmodeling is a family of methods in social network analysis for simplifying a network by partitioning its nodes into blocks, or roles, and summarizing the pattern of ties between blocks as a blockmodel. The goal is to reveal underlying social structures by grouping actors who share similar patterns of relationships.

There are two broad approaches. Direct blockmodeling specifies an ideal blockmodel and searches for a partition that best matches it, often by minimizing a criterion such as least squares for valued networks or misclassification error for binary networks. Indirect blockmodeling first partitions the nodes using clustering criteria and then evaluates how well the resulting partition conforms to a theoretical blockmodel, sometimes refining the partition to improve fit.

Blockmodels rely on notions of equivalence among actors. Structural equivalence requires actors to have identical ties to all others. Regular (or role) equivalence allows actors to have similar types of ties to similar others, without needing identical connections. In practice, blockmodels typically target regular equivalence and produce blocks representing social roles or positions. Blocks may be cohesive (dense internal ties), adjacency blocks (dense between certain blocks), or null blocks (sparse or no ties between blocks).

Applications span mapping organizational structures, analyzing collaboration and communication networks, and studying social segregation. Blockmodeling handles directed and undirected networks and can accommodate binary, valued, or multi-relational data. Limitations include computational complexity, sensitivity to missing data and measurement error, and the challenge of selecting the number of blocks and interpreting the results. Software implementations exist in UCINET, Pajek, and various R and Python packages.