A methodology for applying social network analysis metrics to biological interaction networks

We propose a methodology for applying Social Network Analysis (SNA) metrics to biological Interaction Network studies in the Biodiversity Informatics domain, which may serve as a guide for this activity to other researchers. The methodology is structured into four steps: (i) mapping the data types and the interactions available; (ii) defining the key-questions to be answered and the analysis variables; (iii) choosing the SNA metrics appropriate to the context of the research; and (iv) performing the biological analysis with the support of SNA. Among the material resources used in the development of this research are: SNA metrics (network and species level) and the programs used for its calculation; Statistical Analysis approach (Exploratory Data Analysis and Multivariate Data Analysis) as a support tool; and Business Process Model and Notation (BPMN) to formalize the methodology. From this research, we found that a systematic method to guide the steps one research can facilitate the researchers' works and the interaction with experts from several fields of knowledge. In addition, we noted that there is the possibility of applying this methodology to underexplored knowledge fields.

[1]  Vladimir Batagelj,et al.  Pajek - Program for Large Network Analysis , 1999 .

[2]  M. Emmerson,et al.  MEASUREMENT OF INTERACTION STRENGTH IN NATURE , 2005 .

[3]  Jane Memmott,et al.  Global warming and the disruption of plant-pollinator interactions. , 2007, Ecology letters.

[4]  J. Bascompte,et al.  The modularity of pollination networks , 2007, Proceedings of the National Academy of Sciences.

[5]  A. Saraiva,et al.  Clustering of water bodies in unpolluted and polluted environments based on Escherichia coli phylogroup abundance using a simple interaction database , 2014, Genetics and molecular biology.

[6]  Márcio S Araújo,et al.  Network analysis reveals contrasting effects of intraspecific competition on individual vs. population diets. , 2008, Ecology.

[7]  Werner Ulrich,et al.  A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement , 2008 .

[8]  Pedro Jordano,et al.  Interaction frequency as a surrogate for the total effect of animal mutualists on plants , 2005 .

[9]  L. Freeman.  Centrality in social networks conceptual clarification , 1978 .

[10]  J. Olesen,et al.  Invasion of pollination networks on oceanic islands: importance of invader complexes and endemic super generalists , 2002 .

[11]  Jordi Bascompte,et al.  Asymmetric Coevolutionary Networks Facilitate Biodiversity Maintenance , 2006, Science.

[12]  Martha,et al.  Indices , 1992, Steroids.

[13]  Jane Memmott,et al.  Tolerance of pollination networks to species extinctions , 2004, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[14]  N. Mantel.  The detection of disease clustering and a generalized regression approach. , 1967, Cancer research.

[15]  Jordi Bascompte,et al.  Plant-Animal Mutualistic Networks: The Architecture of Biodiversity , 2007 .

[16]  Carsten F. Dormann,et al.  Indices, Graphs and Null Models: Analyzing Bipartite Ecological Networks , 2009 .

[17]  P. Legendre,et al.  Chapter 7 – Ecological resemblance , 2012 .

[18]  Paulo Guimarães,et al.  Improving the analyses of nestedness for large sets of matrices , 2006, Environ. Model. Softw..

[19]  Wirt Atmar,et al.  The measure of order and disorder in the distribution of species in fragmented habitat , 1993, Oecologia.

[20]  B. Ripley.  Support Functions and Datasets for Venables and Ripley's MASS , 2015 .

[21]  Mark von Rosing,et al.  Business Process Model and Notation - BPMN , 2015, The Complete Business Process Handbook, Vol. I.

[22]  Hadley Wickham,et al.  The Split-Apply-Combine Strategy for Data Analysis , 2011 .

[23]  T. Giannini,et al.  Generalist Bee Species on Brazilian Bee-Plant Interaction Networks , 2012 .

[24]  Ben Shneiderman,et al.  Analyzing Social Media Networks with NodeXL: Insights from a Connected World , 2010 .

[25]  Catherine B. Hurley,et al.  Clustering Visualizations of Multidimensional Data , 2004 .

[26]  Sameer Kumar.  Review of: Hansen, Derek, Shneiderman, Ben, and Smith, Marc A. Analyzing social media networks with NodeXL: insights from a connected world. Massachusetts: Morgan Kaufmann, 2010 , 2011, Inf. Res..

[27]  Luciano Cagnolo,et al.  Uniting pattern and process in plant-animal mutualistic networks: a review. , 2009, Annals of botany.

[28]  Antonio Mauro Saraiva,et al.  Social Network Analysis Metrics and Their Application in Microbiological Network Studies , 2014, CompleNet.

[29]  Carter T. Butts,et al.  Social Network Analysis with sna , 2008 .

[30]  John Scott.  Social Network Analysis , 1988 .

[31]  Shalin Hai-Jew.  Analyzing Social Media Networks with NodeXL: Insights from a Connected World , 2012 .

[32]  M. Braga,et al.  Exploratory Data Analysis , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..