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Blockmodeling provides tools for delineating the essential structure of social networks, a basic objective of social network analysis. The origins of blockmodeling are located in studies of role systems as social networks. The location of an actor in a social network is the set of ties (both present and absent) the actor has with all other actors in the network. Actors having the same set of ties to all other actors are structurally identical. Efficiency in describing a social network flows from viewing all structurally identical locations as forming a position in the social structure.

In an example, let “n” denote the number of actors in a network. When actors are clustered into k positions (with k much smaller than n), the larger network is modeled as a smaller structure by eliminating redundant location descriptions. The partition of the actors also partitions the social ties. All ties from actors in one position to the actors in a second position form a block. Ties within blocks are summarized straightforwardly to describe the relations between pairs of positions. For example, in a school system, if k=2 and the positions are occupied by teachers and administrators, the network ties are partitioned into four blocks: ties among teachers, ties among administrators, ties from teachers to administrators, and ties from administrators to teachers. This simpler network has two positions and four blocks. In general, the simpler network having k positions and k2 blocks is called an image, or a blockmodel, of a network. Representing a network by a blockmodel also provides a precise statement about roles in networks.

Complications in Empirical Networks

Empirical networks are complicated and messy. Therefore, what is needed is a good way of establishing block-models while allowing for departures from “perfect” blockmodels, defined as having actors in positions with identical locations. According to Francois Lorrain and Harrison White, actors are structurally equivalent if they have the same set of ties. To deal with empirical messiness, actors are grouped that are “almost structurally equivalent” by grouping locations that are sufficiently alike into positions. Two basic strategies exist for implementing this approximation: indirect or direct. The indirect strategy has two steps: (1) convert the network into a matrix of (dis)similarities computed for all pairs of locations, and (2) clustering these (dis)similarities. For the first step, Ronald Breiger and colleagues used product-moment correlations, while Ronald Burt used Euclidean distances. In principle, any clustering algorithm can be used for the second step. Three problems arise: (1) using correlations and Euclidean distances can lead to different blockmodels, (2) different clustering algorithms often produce different blockmodels, and (3) there is a lack of good measures of how well these indirectly fitted blockmodels fit the data.

Vladimir Batagelj and colleagues propose a “direct” strategy to partition the network data rather than (dis) similarities. The key step is to answer: what types of blocks are consistent with structural equivalence? In essence, there are only two: null blocks containing only null ties (zeroes), and complete blocks containing only ones. Networks are partitioned directly by identifying blocks close to these ideal block types. Douglas White and Karl Reitz generalized structural equivalence to regular equivalence: loosely, actors are regularly equivalent if they are equivalently connected to equivalent others. (As an example, think of a hierarchical organization: actors at one level are regularly equivalent.) Regular equivalence poses a major problem for the indirect approach: to date, there are no good measures of the extent to which pairs of actors are regularly equivalent. However, regular equivalence is handled easily under the direct strategy because the only permitted block types are null blocks and one-covered blocks. A one-covered block has at least one one in every row and every column.

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