Skip to main content
Log in

Some Simple Algorithms for Structural Comparison

  • Published:
Computational & Mathematical Organization Theory Aims and scope Submit manuscript

Abstract

Structural comparison (i.e., the simultaneous analysis of multiple structures) is a problem which arises frequently in such diverse arenas as the study of organizational forms, social network analysis, and automated text analysis. Prior work has demonstrated the applicability of a range of standard multivariate analysis procedures to the structural comparison problem. Here, some simple algorithms are provided which elucidate several of these methods in an easily implemented form.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abbott, A. and A. Tsay (2000),“Sequence Analysis and Optimal Matching Methods in Sociology: Review and Prospect,” Sociological Methods and Research, 29:3–33.

    Google Scholar 

  • Banks, D. and K.M. Carley (1994),“Metric Inference for Social Networks,” Journal of Classification, 11(1), 121–149.

    Article  Google Scholar 

  • Borg, I. and J. Lingoes (1987), Multidimensional Similarity Structure Analysis., Springer-Verlag, New York.

    Google Scholar 

  • Butts, C.T. (1998),Cluster analysis of unlabeled structures.ICES Research Report 88-04-98, Institute for Complex Engineered Systems, Carnegie Mellon University.

  • Butts, C.T. and K.M. Carley (1998),“Canonical Labeling to Facilitate Graph Comparison,”ICES Research Report 88-06-98, Institute for Complex Engineered Systems, Carnegie Mellon University.

  • Butts, C.T. and K.M. Carley (2001),“Multivariate Methods for Interstructural Analysis,”CASOS working paper, Center for the Computational Analysis of Social and Organization Systems, Carnegie Mellon University.

  • Farris, J.S. (1969),“On the Cophenetic Correlation Coefficient,” Systematic Zoology, 18, 279–285.

    Google Scholar 

  • Gentle, J.E. (1998), Random Number Generation and Monte Carlo Methods, Springer, New York.

    Google Scholar 

  • Hamming, H. (1950),“Error Detecting and Error Correcting Codes,” Bell System Technical Journal, 29, 147–160.

    Google Scholar 

  • Hubert, L.J. (1987), Assignment Methods in Combinatorial Data Analysis, Marcel Dekker, New York.

    Google Scholar 

  • Kirkpatrick, S., C.D. Gelatt, and M.P. Vecchi (1983),“Optimization by Simulated Annealing,” Science, 220, 671–680.

    Google Scholar 

  • Krackhardt, D. (1987a),“Cognitive Social Structures,” Social Networks, 9, 109–134.

    Article  Google Scholar 

  • Krackhardt, D. (1987b),“QAP Partialling as a Test of Spuriousness,” Social Networks, 9, 171–186.

    Article  Google Scholar 

  • Krackhardt, D. (1988),“Predicting With Networks: Nonparametric Multiple Regression Analyses of Dyadic Data,” Social Networks, 10, 359–382.

    Article  Google Scholar 

  • Metropolis, N., A.W. Rosenbluth, M.N. Rosenbluth, A.H. Teller, and E. Teller (1953),“Equation of State Calculations by Fast Computing Machine,” Journal of Chemical Physics, 21, 1087–1091.

    Article  Google Scholar 

  • Otten, R.H.J.M. and L.P.P.P. van Ginneken (1989), The Annealing Algorithm.Kluwer, Boston.

    Google Scholar 

  • Press, W.H., S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery (1992), Numerical Recipes: The Art of Scientific Computing.,Cambridge University Press, Cambridge, second edition.

    Google Scholar 

  • H.C. Romesburg (1984), Cluster Analysis for Researchers., Lifetime Learning Publications, Belmont, California.

    Google Scholar 

  • W.S. Torgerson (1952),“Multidimensional Scaling: I, Theory and Method,” Psychometrika, 17, 401–419.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carter T. Butts.

Additional information

Carter T. Butts is Assistant Professor at the University of California-Irvine in the Department of Sociology, and is a member of the Institute for Mathematical Behavioral Sciences and the California Institute for Telecommunications and Information Technology. His current research focuses on communication during disasters, Bayesian inference for network data, network comparison, and the structure of spatially embedded interpersonal networks.

Kathleen M. Carley is Professor at Carnegie Mellon University, with appointments in the Institute for Software Research International, the H.J. Heinz III School of Public Policy and Management, and the Department of Engineering and Public Policy. Her research centers around areas of social, organizational, knowledge and information networks, organizational design, change, adaptivity and and performance, computational organization theory, crisis management, social theory, impacts on information diffusion of changes in social policy and changes in communication technology, and mapping experts' and executives' knowledge networks using textual analysis techniques.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Butts, C.T., Carley, K.M. Some Simple Algorithms for Structural Comparison. Comput Math Organiz Theor 11, 291–305 (2005). https://doi.org/10.1007/s10588-005-5586-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10588-005-5586-6

Keywords

Navigation