Please use this identifier to cite or link to this item:
|Title:||Social network analysis metrics and their application in microbiological network studies|
|Author:||Silva, Juliana Saragiotto|
Stoppe, Nancy de Castro
Torres, Tatiana Teixeira
Ottoboni, Laura Maria Mariscal
Saraiva, Antonio Mauro
|Abstract:||In the last decade, several researchers have been using interaction networks resources to investigate of the role of biologic interactions in biodiversity maintenance. The conceptual foundations are the same as in Social Networks (such as Facebook), that have brought a set of metrics to study the network structure and the function of each node in the network. Thus, the aim of this work was to assess the application of Social Network Analysis (SNA) concepts and metrics in microbiological interaction networks, to identify patterns of cohesive subgroups, besides discovering new knowledge regarding the underlying structure of subgroups. We built a bipartite microbiological interaction database containing frequency of phylogenetic subgroups in water bodies and applied the following SNA metrics: dependence distribution, strength, betweenness centrality and clique. The sna package for the R program, Pajek, Dieta and Ucinet programs were the tools used. Among the results, we found that SNA concepts and metrics are extremely useful in microbiological studies to understand the correlation between each node in the network (the generalist and the predominant nodes), as well as to analyze the co-occurrence pattern of microorganisms in the network (cohesive subgroups)|
|Subject:||Campos aleatórios de Markov|
|Appears in Collections:||CBMEG - Artigos e Outros Documentos|
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.