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Roberto P. J. Perazzo

Publications and source records attributed to Roberto P. J. Perazzo.

4 recordsLinked to original sources
arXiv2014

Entropic determination of the phase transition in a coevolving opinion-formation model

We study an opinion formation model by the means of a co-evolving complex network where the vertices represent the individuals, characterised by their evolving opinions, and the edges represent the interactions among them. The network adapts to the spreading of opinions in two ways: not only connected agents interact and eventually change their thinking but an agent may also rewire one of its links to a neighborhood holding the same opinion as his. The dynamics depends on an external parameter Φ, which controls the plasticity of the network. We show how the information entropy associated to the distribution of group sizes, allows to locate the phase transition between full consensus and a society where different opinions coexist. We also determine the minimum size of the most informative sampling. At the transition the distribution of the sizes of groups holding the same opinion is scale free.

physics.soc-ph↗
arXiv2007

Analysis and Assembling of Network Structure in Mutualistic Systems

It has been observed that mutualistic bipartite networks have a nested structure of interactions. In addition, the degree distributions associated with the two guilds involved in such networks (e.g. plants & pollinators or plants & seed dispersers) approximately follow a truncated power law. We show that nestedness and truncated power law distributions are intimately linked, and that any biological reasons for such truncation are superimposed to finite size effects . We further explore the internal organization of bipartite networks by developing a self-organizing network model (SNM) that reproduces empirical observations of pollination systems of widely different sizes. Since the only inputs to the SNM are numbers of plant and animal species, and their interactions (i.e., no data on local abundance of the interacting species are needed), we suggest that the well-known association between species frequency of interaction and species degree is a consequence rather than a cause, of the observed network structure.

q-bio.PE↗