Hostname: page-component-76fb5796d-22dnz Total loading time: 0 Render date: 2024-04-25T17:12:19.135Z Has data issue: false hasContentIssue false

Examining the literature on “Networks in Space and in Time.” An introduction

Published online by Cambridge University Press:  09 April 2015

LUCA DE BENEDICTIS
Affiliation:
EIEF and DED, University of Macerata, Italy (e-mail: luca.debenedictis@unimc.it)
MARIA PROSPERINA VITALE
Affiliation:
Department of Economics and Statistics, University of Salerno, Italy (e-mail: mvitale@unisa.it)
STANLEY WASSERMAN
Affiliation:
Departments of Psychology and Statistics, Indiana University, Bloomington, IndianaUSA and Higher School of Economics, National Research University, Moscow, Russia (e-mail: stanwass@indiana.edu)

Abstract

The special issue of “Networks in space and in time: methods and applications” contributes to the debate on contextual analysis in network science. It includes seven research papers that shed light on the analysis of network phenomena studied within geographic space and across temporal dimensions. In these papers, methodological issues as well as specific applications are described from different fields. We take the seven papers, study their citations and texts, and relate them to the broader literature. By exploiting the bibliographic information and the textual data of these seven documents, citation analysis and lexical correspondence analysis allow us to evaluate the connections among the papers included in this issue.

Type
Introduction
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adams, J., Faust, K., & Lovasi, G. S. (Eds.) (2012). Capturing context: Integrating spatial and social network analyses. Social Networks, 34 (1), 15.Google Scholar
Balbi, S., & Stawinoga, A. (2013). Mining the ambiguity: correspondence and network analysis for discovering word sense. SIS Conference 2013 “Advances in Latent Variables. Methods, models and applications”. 19–21 June 2013, Brescia (Italy).Google Scholar
Batagelj, V. (2003). Efficient algorithms for citation network analysis. ArXiv Preprint cs/0309023, 1–27.Google Scholar
Batagelj, V., Doreian, P., Ferligoj, A., & Kejzar, N. (Eds) (2014). Understanding large temporal networks and spatial networks: Exploration, pattern searching, visualization and network evolution. Chichester, UK: John Wiley & Sons Ltd.CrossRefGoogle Scholar
Bodlaj, J., & Batagelj, V. (2014). Network analysis of publications on topological indices from the web of science. Molecular Informatics, 33 (8), 514535.Google Scholar
Brughmans, T. (2013). Networks of networks: A citation network analysis of the adoption, use, and adaptation of formal network techniques in archaeology. Literary and Linguistic Computing, The Journal of Digital Scholarship in the Humanities, 28 (4), 538562.Google Scholar
de Nooy, W. (2003). Fields and networks: Corresponding analysis and social network analysis in the framework of field theory. Poetics, 31 (5), 305327.Google Scholar
de Nooy, W., Mrvar, A., & Batagelj, V. (2011). Exploratory social network analysis with Pajek. Cambridge: Cambridge University Press.Google Scholar
Egghe, L., & Rousseau, R. (2002). Co-citation, bibliographic coupling and a characterization of lattice citation networks. Scientometrics, 55 (3), 349361.Google Scholar
Faust, K., & Skvoretz, J. (2002). Comparing networks across space and time, size and species. Sociological Methodology, 32 (1), 267299.Google Scholar
Garfield, E., Sher, I. H., & Torpie, R. J. (1964). The use of citation data in writing the history of science. Philadelphia: The Institute for Scientific Information Inc.Google Scholar
Hummon, N. P., & Doreian, P. (1989). Connectivity in a citation network: The development of DNA theory. Social Networks, 11 (1), 3963.Google Scholar
Kejzar, N., Cerne, S. K., & Batagelj, V. (2010). Network analysis of works on clustering and classification from web of science. In Locarek-Junge, H., & Weihs, C. (Eds.), Classification as a tool for research (pp. 525536). Berlin Heidelberg: Springer-Verlag.Google Scholar
Lebart, L., & Salem, A. (1988). Statistique textuelle. Paris: Dunod.Google Scholar
Lebart, L., Salem, A., & Berry, L. (1998). Exploring textual data. The Netherlands: Kluwer Academic Publishers.Google Scholar
Leydesdorff, L., Hammarfelt, B., & Salah, A. (2011). The structure of the Arts & humanities citation index: A mapping on the basis of aggregated citations among 1,157 journals. Journal of the American Society for Information Science and Technology, 62 (12), 24142426.Google Scholar
Lucio-Arias, D., & Leydesdorff, L. (2008). Main-path analysis and path-dependent transitions in HistCite-based historiograms. Journal of the American Society for Information Science and Technology, 59 (12), 19481962.CrossRefGoogle Scholar
Snijders, T. A., & Doreian, P. (Eds) (2010). Introduction to the special issue on network dynamics. Social Networks, 32 (1), 13.Google Scholar
Snijders, T. A., & Doreian, P. (Eds) (2012). Introduction to the special issue on network dynamics (Part 2). Social Networks, 34 (3), 289290.Google Scholar
White, H. D. (2011). Scientific and scholarly networks. In Scott, J. & Carrington, P. J. (Eds.), The SAGE handbook of social network analysis (pp. 271285). London: SAGE Publications Ltd.Google Scholar