Citation Networks Dynamics: A New Clustering Algorithm Using Recurrence Plots

The development of science in various fields and the proliferation of the number of scientific studies have prompted researchers to represent the scientific literature as a network whose nodes are works and citations are the links (citation network). In this way, one can more easily detect milestones articles, and areas of greatest scientific activity. One major problem in big citation networks is to be able to carry out an efficient clustering which allows grouping the articles according to their similarities. This method should not be automatic and must not involve a careful reading of the selected works. From here one can see that the problem of finding a proper clustering is not easy to solve. There are several clustering methods with overlapping or not—for a complete review see Fortunato (Phys. Rep., 486:75–174, 2010). In this work we have developed a new clustering method for a citation network using a definition of distance introduced by Bommarito el al. (Physica A, 389:4201–4208, 2010). We have compared this method with the one developed by Pons and Latapy (Computer and information sciences, vol. 3733, pp. 284–293, 2005) applying both to a citation network on sustainability indicators in supply chains. The proposed method proves to be more efficient and, also, through the analysis of the adjacency matrix as a the Recurrence Plot, is able to view the dynamics of the various clusters in their evolution over time

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