Abstract
In this paper we consider methods to highlight clusters in a network structure. Network structures are to-date widely present in transport, telecommunication and, increasingly, in social networks and media. Big amounts of data are being created and methods to find patterns and scheme in the network structure and distribution are required. In this paper we use a sample dataset of passengers’ air traffic flows between European countries to test social network analysis algorithm, comparing them to more classical geographical techniques as the nodal regional analysis.
The paper derives from the joint reflections of the three authors. Giuseppe Borruso wrote paragraphs 2.2, 2.3, 3.1 and 3.2 while Gabriella Schoier realized the other paragraphs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Banfield, J.D., Raftery, A.E.: Model-based Gaussian and non-Gaussian clustering. Biometrics 49, 803–821 (1993)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B (Methodological) 39(1) (1977)
Dasgupta, A., Raftery, A.E.: Detecting features in spatial point processes with cluster via model-based clustering. Journal of the American Statistical Association 93, 294–302 (1988)
Fraley, C., Raftery, A.E.: How many clusters? Which clustering method? Answers via model-based cluster analysis. The Computer Journal 41(8) (1998)
Fraley, C., Raftery, A.E.: Model-based clustering, discriminant analysis and density estimation. Journal of the American Statistical Association 97(458) (2002)
Fraley, C., Raftery, A.E.: MCLUST Version 4 for R: normal mixture modeling and model-based clustering, classification and density estimation, Technical Report no. 597. Department of Statistics, University of Washington (2012)
Fung, G.: A comprehensive overview of basic clustering algorithms (2001), http://pages.cs.wisc.edu/~gfung/ (cited October 2012)
Han, J., Kamber, M., Tung, A.K.H.: Spatial clustering methods in data mining. A survey (2001), http://www.cs.uiuc.edu/homes/hanj/ (cited December 2012)
Hoyle, B.S., Knowles, R.D.: Modern transport geography, 2nd edn. Wiley, Chichester (1998)
Knowles, R.D., Hall, D.: Transport deregulation and privatization. In: Hoyle, B.S., Knowles, R.D. (eds.) Modern Transport Geography, 2nd edn., Wiley, Chichester (1998)
Kochen, M.: The small World. Ablex Publishing Corporation, Norwood (1989)
Koperski, K., Han, J., Adhikary, J. : Mining Knowledge in Geographical Data (1998), http://www.cs.uiuc.edu/homes/hanj/pdf/geo_survey98.pdf (retrieved October 2011)
Miller, H.: A measurement theory for time geography. Geographical Analysis 37, 17–45 (2005)
Moody, J.: Peer influence groups: identifying dense clusters in large networks. Social Networks 23, 261–283 (2001)
Nystuen, J.D., Dacey, M.F.: A graph theory interpretation of nodal regions. Papers and Proceedings of the Regional Science Association 7, 29–42 (1968)
O’Sullivan, D., Unwin, P.J.: Geographic Information Analysis. Wiley, Chichester (2003)
Rabino., G., and Occelli, S.: Understanding spatial structure from network data: Theoretical considerations and applications. Cybergeo, 29 (1997) http://www.cybergeo.eu/index2199.html
Schoier, G.: Blockmodeling Techniques for Web Mining. In: Haerdle, W., Roenz, B. (eds.) Proceedings of Compstat 2002. Springer, Berlin (2002)
Schoier, G., Melfi, G.: Clusters d’ensemble de données larges dans le Web Log Mining. In: Attidel 10mes Rencontres de la Société Francophone de Classification, Neuchâtel Settembre., pp. 160–164 (2003)
Schoier, G., Borruso, G.: A Clustering Method for Large Spatial Databases. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds.) ICCSA 2004. LNCS, vol. 3044, pp. 1089–1095. Springer, Heidelberg (2004)
Schoier, G., Melfi, G.: A Different Approach for the Analysis of Web Access Logs. In: Vichi, M., Monari, P., Mignani, S., Montanari, A. (eds.) New Developments in Classification and Data Analysis. Springer (2004)
Schoier, G.: An Algorithm for the individuation of clusters of units in large databases. In: Atti del Quarto Convegno su Modelli Complessi e Metodi Computazionali Intensivi per la Stima e la Previsione (S.CO. 2005), Bressanone (2005)
Scott, J.: Social Network Analysis. Sage, NewburyPark (1992)
Trasarti, R., Giannotti, F., Nanni, M., Pedreschi, D., Renso, C.: A Query Language for Mobility Data Mining. International Journal of Data Warehousing and Mining 7(1), 24–45 (2011)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, New York (1994)
Weisstein, E.W.: Graph Diameter, MathWorld–A Wolfram Web Resource, http://mathworld.wolfram.com/GraphDiameter.html (accessed January, 2011)
Borruso, G.: A nodal regional analysis of air passenger transport in Europe. International Journal of Business Intelligence and Data Mining 8(4), 377–396 (2013)
Borruso, G.: Polarization and Hierarchies in Air Passenger Transport in Europe. In: Murgante, B., Misra, S., Carlini, M., Torre, C.M., Nguyen, H.-Q., Taniar, D., Apduhan, B.O., Gervasi, O. (eds.) ICCSA 2013, Part IV. LNCS, vol. 7974, pp. 389–402. Springer, Heidelberg (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Schoier, G., Borruso, G. (2014). European Air Traffic: A Social and Geographical Network Analysis. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8582. Springer, Cham. https://doi.org/10.1007/978-3-319-09147-1_33
Download citation
DOI: https://doi.org/10.1007/978-3-319-09147-1_33
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09146-4
Online ISBN: 978-3-319-09147-1
eBook Packages: Computer ScienceComputer Science (R0)