{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T02:40:13Z","timestamp":1685500813850},"reference-count":30,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Bifurcation Chaos"],"published-print":{"date-parts":[[2010,3]]},"abstract":"The topology of real-world complex networks, such as in transportation and communication, is always changing with time. Such changes can arise not only as a natural consequence of their growth, but also due to major modifications in their intrinsic organization. For instance, the network of transportation routes between cities and towns (hence locations) of a given country undergo a major change with the progressive implementation of commercial air transportation. While the locations could be originally interconnected through highways (paths, giving rise to geographical networks), transportation between those sites progressively shifted or was complemented by air transportation, with scale free characteristics. In the present work we introduce the path-star transformation (in its uniform and preferential versions) as a means to model such network transformations where paths give rise to stars of connectivity. It is also shown, through optimal multivariate statistical methods (i.e. canonical projections and maximum likelihood classification) that while the US highways network adheres closely to a geographical network model, its path-star transformation yields a network whose topological properties closely resembles those of the respective airport transportation network.<\/jats:p>","DOI":"10.1142\/s0218127410026083","type":"journal-article","created":{"date-parts":[[2010,3,18]],"date-time":"2010-03-18T09:23:27Z","timestamp":1268904207000},"page":"795-804","source":"Crossref","is-referenced-by-count":1,"title":["MODELING THE EVOLUTION OF COMPLEX NETWORKS THROUGH THE PATH-STAR TRANSFORMATION AND OPTIMAL MULTIVARIATE METHODS"],"prefix":"10.1142","volume":"20","author":[{"given":"L.","family":"DA F. COSTA","sequence":"first","affiliation":[{"name":"Institute of Physics at S\u00e3o Carlos, University of S\u00e3o Paulo, PO Box 369, S\u00e3o Carlos, S\u00e3o Paulo, 13560-970, Brazil"},{"name":"National Institute of Science and Technology for Complex Systems, Brazil"}]},{"given":"F. A.","family":"RODRIGUES","sequence":"additional","affiliation":[{"name":"Departamento de Matem\u00e1tica Aplicada e Estat\u00edstica, Instituto de Ci\u00eancias Matem\u00e1ticas e de Computa\u00e7\u00e3o, Universidade de S\u00e3o Paulo, Campus de S\u00e3o Carlos, Caixa Postal 668, 13560-970 S\u00e3o Carlos, SP, Brazil"}]},{"given":"P. R.","family":"VILLAS BOAS","sequence":"additional","affiliation":[{"name":"Institute of Physics at S\u00e3o Carlos, University of S\u00e3o Paulo, PO Box 369, S\u00e3o Carlos, S\u00e3o Paulo, 13560-970, Brazil"}]}],"member":"219","published-online":{"date-parts":[[2012,5,2]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.200327197"},{"key":"rf2","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1126\/science.286.5439.509","volume":"286","author":"Barab\u00e1si A.-L.","journal-title":"Science"},{"key":"rf4","series-title":"Information Science and Statistics","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop C. 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