Deterministic Blockmodeling of Two-Mode Binary Networks Using a Two-Mode KL-Median Heuristic

Abstract Deterministic blockmodeling of a two-mode binary network matrix based on structural equivalence is a well-known problem in the social network literature. Whether implemented in a standalone fashion, or embedded within a metaheuristic framework, a popular relocation heuristic (RH) has served as the principal solution tool for this problem. In this paper, we establish that a two-mode KL-median heuristic (TMKLMedH) seeks to optimize the same criterion as the RH for deterministic blockmodeling. The TMKLMedH runs much faster than the RH, so many more restarts of the TMKLMedH can be accomplished when the two methods are constrained to the same time limit. Three computational comparisons of RH and TMKLMedH were conducted using both synthetic and real-world networks. In all three comparisons, the superiority of TMKLMedH was unequivocal.