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A Tabu-Search Heuristic for Deterministic Two-Mode Blockmodeling of Binary Network Matrices.

Michael Brusco1, Douglas Steinley2

  • 1Department of Marketing, College of Business, Florida State University, Tallahassee, FL, 32306-1110, USA. mbrusco@fsu.edu.

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This study introduces a tabu search heuristic for analyzing two-mode binary data in social networks. The new method outperforms traditional relocation heuristics in identifying structural equivalences for better blockmodeling.

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Area of Science:

  • Social Network Analysis
  • Computational Social Science
  • Network Science

Background:

  • Two-mode binary data matrices are common in social networks, representing relationships like attendance or participation.
  • Two-mode blockmodeling using structural equivalence aims to partition row and column objects into blocks that are ideally complete (all 1s) or null (all 0s).
  • Current predominant methods rely on object relocation heuristics with multiple restarts to minimize inconsistencies with ideal block structures.

Purpose of the Study:

  • To propose a novel, fast, and effective tabu search implementation as an alternative to existing methods for two-mode blockmodeling.
  • To evaluate the performance of the proposed tabu search heuristic against the traditional relocation heuristic.

Main Methods:

  • Developed a tabu search heuristic for two-mode blockmodeling based on structural equivalence.
  • Compared the tabu search heuristic with a standard object relocation heuristic.
  • Utilized a set of 48 large network matrices for computational comparisons.

Main Results:

  • The tabu search heuristic consistently achieved better objective function values than the relocation heuristic.
  • Performance improvements were observed under identical computation time constraints.
  • The tabu search implementation proved to be both fast and effective.

Conclusions:

  • Tabu search offers a superior alternative for two-mode blockmodeling, improving the identification of structural equivalences.
  • The proposed heuristic provides a more efficient and effective approach to analyzing complex social network data.
  • This advancement can enhance the understanding of network structures in various social contexts.