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Multiple factor analysis for time-varying two-mode networks

Published online by Cambridge University Press:  12 February 2015

GIANCARLO RAGOZINI ,
DOMENICO DE STEFANO  and
MARIA ROSARIA D'ESPOSITO
GIANCARLO RAGOZINI
Affiliation:
Department of Political Science, University of Naples Federico II, Naples, Italy (e-mail: giancarlo.ragozini@unina.it)
DOMENICO DE STEFANO
Affiliation:
Department of Political and Social Sciences DiSPeS, University of Trieste, Trieste, Italy (e-mail: ddestefano@units.it)
MARIA ROSARIA D'ESPOSITO
Affiliation:
Department of Economics and Statistics, University of Salerno, Fisciano, Italy (e-mail: mdesposito@unisa.it)
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Abstract

Most social networks present complex structures. They can be both multi-modal and multi-relational. In addition, each relationship can be observed across time occasions. Relational data observed in such conditions can be organized into multidimensional arrays and statistical methods from the theory of multiway data analysis may be exploited to reveal the underlying data structure. In this paper, we adopt an exploratory data analysis point of view, and we present a procedure based on multiple factor analysis and multiple correspondence analysis to deal with time-varying two-mode networks. This procedure allows us to create static displays in order to explore network evolutions and to visually analyze the degree of similarity of actor/event network profiles over time while preserving the different statuses of the two modes.

Keywords

multiple factor analysismultiple correspondence analysistwo-mode networkstime-varying networks
Type
Research Article
Information
Network Science , Volume 3 , Issue 1: Networks in Space and in Time: Methods and Applications , March 2015 , pp. 18 - 36
Copyright
Copyright © Cambridge University Press 2015 

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References

Abascal, E., García Lautre, I., & Landaluce, M. I. (2006). Multiple factor analysis of mixed tables of metric and categorical data. In Greenacre, M., & Blasius, J. (Eds.), Multiple Correspondence Analysis and Related Methods (pp. 351–367). Boca Raton, FL: Chapman & Hall/CRC Taylor & Francis Group.CrossRefGoogle Scholar
Abdi, H., Williams, L. J., Valentin, D. & Bennani-Dosse, M. (2012). STATIS and DISTATIS: Optimum multi-table proncipal component analysis and three-way metric multidimensional scaling. WIREs Computational Statistics, 4 (2), 124–167.CrossRefGoogle Scholar
Abdi, H., Williams, L. J., & Valentin, D. (2013). Multiple factor analysis: Principal component analysis for multitable and multiblock data sets. WIREs Computational Statistics, 5 (2), 149–179.CrossRefGoogle Scholar
Batagelj, V., & Mrvar, A. (1998). Pajek – program for large network analysis. Connections, 21, 47–57.Google Scholar
Baur, M., Benkert, M., Brandes, U., Cornelsen, S., Gaertler, M., Köpf, B., Lerner, J., & Wagner, D. (2002). Visone software for visual social network analysis. In Mutzel, P., Junger, M., & Leipert, S. (Eds.), Graph Drawing Software, Lecture Notes in Computer Science, volume 2265 (pp. 463–464). Berlin Heidelberg: Springer.Google Scholar
Bender-deMoll, S., & McFarland, D. (2006). The art and science of dynamic network visualization. Journal of Social Structure, 7 (2).Google Scholar
Borgatti, S. P., & Halgin, D. (2011). Analyzing affiliation networks. In Carrington, P., & Scott, J. (Eds.), The Sage Handbook of Social Network Analysis (pp. 417–433). London: Sage Publications.Google Scholar
Brandes, U., Fleischer, D., & Puppe, T. (2007). Dynamic spectral layout with an application to small words. Journal of Graph and Applications, 11, 325–343.Google Scholar
Conaldi, G., Lomi, A., & Tonellato, M. (2012). Dynamic models of affiliation and the network structure of problem solving in an open source software project. Organizational Research Methods, 15, 385–412.CrossRefGoogle Scholar
Coppi, R. (1994). An introduction to multiway data and their analysis. Computational Statistics & Data Analysis, 18, 3–13.CrossRefGoogle Scholar
Correa, C. D., & Ma, K.-L. (2011). Visualizing social networks. In Aggarwal, C. C. (Ed.), Social Network Data Analytics (pp. 307–336). New York: Springer.CrossRefGoogle Scholar
Davis, D., Lichtenwalter, R., & Chawla, N. V. (2011). Multi-relational link prediction in heterogeneous information networks. In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Kaohsiung, Taiwan, 281–288.CrossRefGoogle Scholar
D'Esposito, M. R., De Stefano, D., & Ragozini, G. (2014a). On the use of multiple correspondence analysis to visually explore affiliation networks, Social Networks, 38, 28–40.CrossRefGoogle Scholar
D'Esposito, M. R., De Stefano, D., & Ragozini, G. (2014b). Multiple factor analysis to visually explore collaboration structures: The case of technological districts. Proceedings of the XLVII Scientific Meeting of Italian Statistical Society, Cleup, Padova, 1–6.Google Scholar
D'Esposito, M. R., De Stefano, D., & Ragozini, G. (2014c). A comparison of χ2 metrics for the assessment of relational similarities in affiliation networks. In Vicari, D., Okada, A., Ragozini, G., & Weihs, C. (Eds.), Analysis and Modeling of Complex Data in Behavioral and Social Sciences, Studies in Classification, Data Analysis, and Knowledge Organization (pp. 113–122). Cham, Switzerland: Springer International Publishing.Google Scholar
Escofier, B., & Pagés, J. (1988). Analyses Factorielles Simples et Multiples: Objectifs, Méthodes, Interprétation. Paris: Dunod.Google Scholar
Escofier, B., & Pagés, J. (1994). Multiple factor analysis (AFMULT package). Computational Statistics and Data Analysis, 18, 121–140.CrossRefGoogle Scholar
Faust, K., Willert, K. E., Rowlee, D. D., & Skvoretz, J. (2002). Scaling and statistical models for affiliation networks: Patterns of participation among Soviet politicians during the Breznev era. Social Networks, 24, 231–259.CrossRefGoogle Scholar
Freeman, L. C. (2000). Visualizing social networks. Journal of Social Structure, 1 (1).Google Scholar
Gagliolo, M., Lenaerts, T., & Jacobs, D. (2014). Politics matters. Dynamics of inter-organizational networks among immigrant associations. In Contucci, P., Menezes, R., Omicini, A., & Poncela-Casasnovas, J. (Eds.), Complex Networks V, volume 549 of Studies in Computational Intelligence (pp. 47–55). Cham, Switzerland: Springer International Publishing.CrossRefGoogle Scholar
Gabriel, K. R. (1995). Biplot display of multivariate categorical data, with comments on multiple correspondence analysis. In Krzanowsky, W. (Ed.), Recent Advances in Descriptive Multivariate Analysis (pp. 190–226). Oxford: Oxford Science Publications.CrossRefGoogle Scholar
Ghani, S., Elmqvist, N., & Yi, J. S. (2012). Perception of animated node-link diagrams for dynamic graphs, Computer graphics Forum, 31, 1205–1214.CrossRefGoogle Scholar
Gordon, R. A., & Heinrich, C. J. (2004). Modelling trajectories in social program outcomes for performance accountability. American Journal of Evaluation, 25, 161–189.CrossRefGoogle Scholar
Gower, J. C. (2006). Divided by a common language: Analyzing and visualizing two-way arrays. In Greenacre, M., & Blasius, J. (Eds.), Multiple Correspondence Analysis and Related Methods (pp. 77–105). Boca Raton, FL: Chapman & Hall/CRC Taylor & Francis Group.CrossRefGoogle Scholar
Greenacre, M. (2006). From simple to multiple correspondence analysis. In Greenacre, M., & Blasius, J. (Eds.), Multiple Correspondence Analysis and Related Methods (pp. 41–76). Boca Raton, FL: Chapman & Hall/CRC Taylor & Francis Group.CrossRefGoogle Scholar
Greenacre, M. (2010). Biplots in Practice. Madrid: Fundación BBVA.Google Scholar
Holme, P., Park, S. M., Kim, B. J., & Edling, C. R. (2007). Korean university life in a network perspective: Dynamics of a large affiliation network. Physica A, 373, 821–830.CrossRefGoogle Scholar
Horvat, E. A., & Zweig, K. A. (2012). One-mode projection of multiplex bipartite graphs. In IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 599–606. Istanbul.CrossRefGoogle Scholar
Kang, H., Getoor, L., & Singh, L. (2007). Visual analysis of dynamic group membership in temporal social networks. SIGKDD, 9, 13–21.CrossRefGoogle Scholar
Koskinen, J., & Edling, C. R. (2012). Modeling the evolution of a bipartite network –Peer referral in interlocking directorates. Social Networks, 34, 309–322.CrossRefGoogle ScholarPubMed
Kroonenberg, P. M. (2008). Applied multiway data analysis. New York: Wiley.CrossRefGoogle Scholar
Leydesdorff, L. & Schank, T. (2008). Dynamic animations of journal maps: Indicators of structural change and interdisciplinary developments. Journal of the American Society for Information Science and Technology, 59, 1810–1818.CrossRefGoogle Scholar
McFarland, D. A. (1999). Organized behavior in social systems: A study of student engagement and resistance in high schools. Doctoral Dissertation, Department of Sociology, University of Chicago.Google Scholar
Memon, B. R., & Wiil, U. K. (2013). Visual analysis of heterogeneous networks. In European Intelligence and Security Informatics Conference, Uppsala: IEEE Computer Society Press, 129–134.Google Scholar
Moody, J., McFarland, D., & Bender-deMoll, S. (2005). Dynamic network visualization. American Journal of Sociology, 110, 1206–1241.CrossRefGoogle Scholar
Nenadic, O., & Greenacre, M. (2007). Correspondence analysis in R, with two- and three-dimensional graphics: The ca package. Journal of Statistical Software, 20, 1–13.Google Scholar
Opsahl, T. (2013). Triadic closure in two-mode networks: Redefining the global and local clustering coefficients. Social Networks, 35, 159–167.CrossRefGoogle Scholar
Perer, A., & Shneiderman, B. (2006). Balancing systematic and flexible exploration of social networks. IEEE Transaction on Visualization and Computer Graphics, 12, 693–700.CrossRefGoogle ScholarPubMed
Richards, W., & Seary, A. (2000). Eigen analysis of networks, Journal of Social Structure, 1.Google Scholar
Robert, P., & Escoufier, Y. (1976). A unifying tool for linear multivariate statistical methods: The RV-coefficient. Journal of Royal Statistical Society. Applied Statistics, 25, 257–265.Google Scholar
Seierstad, C., & Opsahl, T. (2011). For the few not the many? The effects of affirmative action on presence, prominence, and social capital of women directors in Norway. Scandinavian Journal of Management, 27, 44–54.CrossRefGoogle Scholar
Snijders, T. A. B., Lomi, A., & Torló, V. J. (2013). A model for the multiplex dynamics of two-mode and one-mode networks, with an application to employment preference, friendship, and advice. Social Network, 35, 265–276.CrossRefGoogle Scholar
SPAD.TM Version 5.5 (2002). Computer Program, Decisia, Pantin, France.Google Scholar
Tversky, B., Bauer Morrison, J., & Betrancourt, M. (2002). Animation: Can it facilitate? International Journal of Human-Computer Studies, 57, 247–262.CrossRefGoogle Scholar