Social Network Analysis for Behavioural Scientists using R (SNAR01)

Full details can be found at;
http://bit.ly/2DEVnDlSOCIAL_NETWORK_ANALYSIS

This course will take place in Glasgow city entre for 2nd - 6th July 2018 
and will be delivered by Prof. James Curley.

Course Overview:
This workshop will provide students with the opportunity to learn how to 
use social network analysis to analyze social relational datasets such as 
human friendship networks or animal social networks. Attendees will learn 
how to use R and several R packages including igraph, sna, network, asnipe, 
timeordered, tsna to create network graphs, calculate descriptive network 
metrics, use randomization and random models to evaluate the significance 
of these metrics, determine graph structural properties including community 
structures, use QAP and MRQAP methods to assess how network characteristics 
relate to other individual and relational attributes, and measure change 
over time in dynamic networks. Attendees will also learn how to produce 
high quality network visualizations using R.

Monday 2nd
Elementary concepts.
Module 1: Introduction to Social Networks Theory. This will cover central 
themes of social network analysis: the major data formats, structures and 
collection methods, the different types of graphs and networks; how to 
generate and visualize social networks and generate basic descriptive 
statistics, and what hypotheses and questions can be addressed using social 
network analysis. We will also discuss data types and questions of interest 
to attendees.

Module 2: R refresher and R packages. This module will provide a quick 
overview of the major packages used for social network analysis in R 
including ‘igraph’, ‘sna’, ‘network’. We shall learn how to convert raw 
data formats to network objects in R; how to interface with R network 
objects and how to create simple network visualizations.
Module 3: Intro to Visualizing Networks. We shall cover how to generate and 
beautify networks using the ‘igraph’ R package covering issues such as 
layout decisions, coloring and sizing of nodes and edges by network 
attributes, metrics or community. We shall extend this to cover how to 
create dynamic three-dimentional network plots using the R 
package ‘threejs’. We shall also discuss how to use the ‘ggplot’ 
based ‘ggraph’ R package which has many customizable features for plotting 
networks.

Tuesday 3rd
Basic analyses.
Module 4: Identifying important nodes and edges. Learn how to evaluate key 
indicators of each node’s significance to the network including, degree 
centrality, eigenvector centrality, power centrality, and betweenness. 
Describe how to calculate for directed vs. undirected and weighted vs 
unweighted networks. Learn how to assess simple relationships between nodes 
including geodesic distances, identifying neighbors, determining shortest 
and longest paths.

Module 5: Introduction to Network Randomization and Random Models. It is 
critical in network analysis to evaluate how likely it is to observe a 
given network metric for our network of interest. This module will 
introduce how to use null models (randomizations/permutations or random 
graphs) to test whether networks have characteristics that are especially 
surprising after accounting for non-independence. We also will learn how to 
generate confidence intervals for network metrics and carry out 
significance testing. We shall learn how to use the ‘igraph’ package for 
random graph generation.

Module 6: Network Graph Characteristics. We shall cover concepts such as 
dyad and triad censuses, transitivity, assortativity, homophily, 
reciprocity, clustering and density. We shall discuss their significance 
and importance for answering relevant questions to the patterns of social 
associations and behavior in networks.

Wednesday 4th
Extending Network Analysis.
Module 7: Community Detection. Overview of what communities (modules) mean 
for animal and human social networks – that a high proportion of nodes or 
edge weights cluster within a sub-group of nodes/edges rather than between 
sub-groups. We shall review the numerous community detection methods and 
describe the implementation of major ones in R. How to generate robustness 
in evaluation of community detection. How to to determine the degree of 
community structure in a network using the index of modularity (Q) and 
bootstrapping techniques such as community assortativity (rcom). 
Hierarchical clustering for analysis of hierarchically organized social 
societies.

Module 8: Randomizations and Random models II. This module will further 
explore how to determine the appropriate choice of null models for 
behavioral data. This is not always a trivial exercise for behavioral 
datasets. We will use the ‘asnipe’ R package for network permutation 
and ‘igraph’ R package for null model generation. We shall also cover 
options for dealing with missing data, low sampling rates and pseudo-
replication, options for data imputation, and how to account for temporal 
structure in data randomizations.

Thursday 5th
Advanced Methods.
Module 9: Quadratic Assignment Procedure (QAP) Regression. Using QAP 
regression to control for non-independence of data when comparing network 
position or strength between networks or comparing individual/dyadic 
network metrics with other individual/dyadic attributes. Extending analyses 
to using multiple regression quadratic assignment procedure (MRQAP). How to 
perform in base-R and using the ‘asnipe’ R package. How to generate effect 
sizes when using QAP and MRQAP.

Module 10: Visualizing Networks II. This module will tackle advanced 
options for network plotting, including how to export ‘igraph’ R objects to 
Gephi for generating even more beautiful customized plots, how to create 
interactive web based network visualizations using R packages such 
as ‘threejs’, ‘visNetwork’ and ‘networkD3’, and how to plot or animate 
dynamic social networks.

Module 11: Dynamic Networks. Key questions that are often neglected are how 
do network structures remain stable or change over time and can we infer 
how meaningful this stability and instability is? This module will discuss 
various methods for analysis of change for time-ordered and time-aggregated 
networks. We will use R packages for analysis of dynamic networks 
including ‘timeordered’, ‘networkDynamic’ and ‘tsna’.

Email questions to oliverhoo...@pssatsitcis.com

Check out our sister sites,
www.PRstatistics.com (Ecology and Life Sciences)
www.PRinformatics.com (Bioinformatics and data science)
www.PSstatsistics.com (Behaviour and cognition) 


1.      April 9th – 13th 2018 
NETWORK ANAYLSIS FOR ECOLOGISTS USING R (NTWA02
Glasgow, Scotland, Dr. Marco Scotti   
www.prstatistics.com/course/network-analysis-ecologists-ntwa02/

2.      April 16th – 20th 2018
INTRODUCTION TO STATISTICAL MODELLING FOR PSYCHOLOGISTS USING R (IPSY01)
Glasgow, Scotland, Dr. Dale Barr, Dr Luc Bussierre   
http://www.psstatistics.com/course/introduction-to-statistics-using-r-for-
psychologists-ipsy01/

3.      April 23rd – 27th 2018
MULTIVARIATE ANALYSIS OF ECOLOGICAL COMMUNITIES USING THE VEGAN PACKAGE 
(VGNR01)
Glasgow, Scotland, Dr. Peter Solymos, Dr. Guillaume Blanchet             
www.prstatistics.com/course/multivariate-analysis-of-ecological-communities-
in-r-with-the-vegan-package-vgnr01/

4.      April 30th – 4th May 2018
QUANTITATIVE GEOGRAPHIC ECOLOGY: MODELING GENOMES, NICHES, AND COMMUNITIES 
(QGER01)
Glasgow, Scotland, Dr. Dan Warren, Dr. Matt Fitzpatrick
www.prstatistics.com/course/quantitative-geographic-ecology-using-r-
modelling-genomes-niches-and-communities-qger01/

5.      May 7th – 11th 2018 ADVANCES IN MULTIVARIATE ANALYSIS OF SPATIAL 
ECOLOGICAL DATA USING R (MVSP02)
CANADA (QUEBEC), Prof. Pierre Legendre, Dr. Guillaume Blanchet
www.prstatistics.com/course/advances-in-spatial-analysis-of-multivariate-
ecological-data-theory-and-practice-mvsp03/
6.      May 14th - 18th 2018
INTRODUCTION TO MIXED (HIERARCHICAL) MODELS FOR BIOLOGISTS (IMBR01)
CANADA (QUEBEC), Prof Subhash Lele 
www.prstatistics.com/course/introduction-to-mixed-hierarchical-models-for-
biologists-using-r-imbr01/

7.      May 21st - 25th 2018
INTRODUCTION TO PYTHON FOR BIOLOGISTS (IPYB05)
SCENE, Scotland, Dr. Martin Jones
http://www.prinformatics.com/course/introduction-to-python-for-biologists-
ipyb05/

8.      May 21st - 25th 2018
INTRODUCTION TO REMOTE SENISNG AND GIS FOR ECOLOGICAL APPLICATIONS (IRMS01)
Glasgow, Scotland, Prof. Duccio Rocchini, Dr. Luca Delucchi
www.prinformatics.com/course/introduction-to-remote-sensing-and-gis-for-
ecological-applications-irms01/

9.      May 28th – 31st 2018
STABLE ISOTOPE MIXING MODELS USING SIAR, SIBER AND MIXSIAR (SIMM04)
CANADA (QUEBEC) Dr. Andrew Parnell, Dr. Andrew Jackson 
www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm04/

10.     May 28th – June 1st 2018
ADVANCED PYTHON FOR BIOLOGISTS (APYB02)
SCENE, Scotland, Dr. Martin Jones
www.prinformatics.com/course/advanced-python-biologists-apyb02/

11.     June 12th - 15th 2018
SPECIES DISTRIBUTION MODELLING (DBMR01)
Myuna Bay sport and recreation, Australia, Prof. Jane Elith, Dr. Gurutzeta 
Guillera
www.prstatistics.com/course/species-distribution-models-using-r-sdmr01/

12.     June 18th – 22nd 2018
STRUCTURAL EQUATION MODELLING FOR ECOLOGISTS AND EVOLUTIONARY BIOLOGISTS 
USING R (SEMR02)
Myuna Bay sport and recreation, Australia, Dr. Jon Lefcheck
www.prstatistics.com/course/structural-equation-modelling-for-ecologists-
and-evolutionary-biologists-semr02/

13.     June 25th – 29th 2018
SPECIES DISTRIBUTION/OCCUPANCY MODELLING USING R (OCCU01)
Glasgow, Scotland, Dr. Darryl McKenzie
www.prstatistics.com/course/species-distributionoccupancy-modelling-using-r-
occu01/

14.     July 2nd - 5th 2018
SOCIAL NETWORK ANALYSIS FOR BEHAVIOURAL SCIENTISTS USING R (SNAR01)
Glasgow, Scotland, Prof James Curley
http://www.psstatistics.com/course/social-network-analysis-for-behavioral-
scientists-snar01/

15.     July 8th – 12th 2018
MODEL BASE MULTIVARIATE ANALYSIS OF ABUNDANCE DATA USING R (MBMV02)
Glasgow, Scotland, Prof David Warton
www.prstatistics.com/course/model-base-multivariate-analysis-of-abundance-
data-using-r-mbmv02/

16.     July 16th – 20th 2018
PRECISION MEDICINE BIOINFORMATICS: FROM RAW GENOME AND TRANSCRIPTOME DATA 
TO CLINICAL INTERPRETATION (PMBI01)
Glasgow, Scotland, Dr Malachi Griffith, Dr. Obi Griffith
www.prinformatics.com/course/precision-medicine-bioinformatics-from-raw-
genome-and-transcriptome-data-to-clinical-interpretation-pmbi01/

17.     July 23rd – 27th 2018
EUKARYOTIC METABARCODING (EUKB01)
Glasgow, Scotland, Dr. Owen Wangensteen
http://www.prinformatics.com/course/eukaryotic-metabarcoding-eukb01/

18.     October 8th – 12th 2018
INTRODUCTION TO SPATIAL ANALYSIS OF ECOLOGICAL DATA USING R (ISAE01)
Glasgow, Scotland, Prof. Subhash Lele
https://www.prstatistics.com/course/introduction-to-spatial-analysis-of-
ecological-data-using-r-isae01/

19.     October 15th – 19th 2018
APPLIED BAYESIAN MODELLING FOR ECOLOGISTS AND EPIDEMIOLOGISTS (ABME
Glasgow, Scotland, Dr. Matt Denwood, Emma Howard
http://www.prstatistics.com/course/applied-bayesian-modelling-ecologists-
epidemiologists-abme04/

20.     October 29th – November 2nd 2018
PHYLOGENETIC COMPARATIVE METHODS FOR STUDYING DIVERSIFICATION AND 
PHENOTYPIC EVOLUTION (PCME01)
Glasgow, Scotland, Prof. Subhash Lele
Dr. Antigoni Kaliontzopoulou
https://www.prstatistics.com/course/phylogenetic-comparative-methods-for-
studying-diversification-and-phenotypic-evolution-pcme01/

21.     November 26th – 30th 2018
FUNCTIONAL ECOLOGY FROM ORGANISM TO ECOSYSTEM: THEORY AND COMPUTATION (FEER
Glasgow, Scotland, Dr. Francesco de Bello, Dr. Lars Götzenberger, Dr. 
Carlos Carmona
http://www.prstatistics.com/course/functional-ecology-from-organism-to-
ecosystem-theory-and-computation-feer01/

22.     February 2018 TBC
MOVEMENT ECOLOGY (MOVE02)
Margam Discovery Centre, Wales, Dr Luca Borger, Dr Ronny Wilson, Dr 
Jonathan Potts
www.prstatistics.com/course/movement-ecology-move01/

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