What happens if we decide to plot sequences as graphs? Can this approach increase our knowledge of the underlying structure common to the single sequences? Moreover, will this approach bring to the surface the structures, patterns, and careers still (perhaps) hidden to our eyes and knowledge? The use of graphic visualization and the social network analysis suggested in this paper have two purposes. One is to find new ways to present results; the other is to gain a new perspective from which to observe sequences. To do this, however, we need to see sequences not as individuals moving from one state to another but as individuals who exhibit common career patterns. This proposal starts from the intent to find a new way to observe how careers develop over time and thus to capture their dynamic evolution. To this end, we need to give physical form to sequences and their underlying generative processes: that is to say, we must convert sequences into objects—networks graphs—with which it is possible to explore how they evolve.
Sequence as Network: An Attempt to Apply Network Analysis to Sequence Analysis / Bison, Ivano. - STAMPA. - 2:(2014), pp. 231-248. [10.1007/978-3-319-04969-4_12]
Sequence as Network: An Attempt to Apply Network Analysis to Sequence Analysis
Bison, Ivano
2014-01-01
Abstract
What happens if we decide to plot sequences as graphs? Can this approach increase our knowledge of the underlying structure common to the single sequences? Moreover, will this approach bring to the surface the structures, patterns, and careers still (perhaps) hidden to our eyes and knowledge? The use of graphic visualization and the social network analysis suggested in this paper have two purposes. One is to find new ways to present results; the other is to gain a new perspective from which to observe sequences. To do this, however, we need to see sequences not as individuals moving from one state to another but as individuals who exhibit common career patterns. This proposal starts from the intent to find a new way to observe how careers develop over time and thus to capture their dynamic evolution. To this end, we need to give physical form to sequences and their underlying generative processes: that is to say, we must convert sequences into objects—networks graphs—with which it is possible to explore how they evolve.File | Dimensione | Formato | |
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