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Social Network Analysis for Behavioural Scientists using R


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Social Network Analysis for Behavioural Scientists using R (SNAR01)

https://www.psstatistics.com/course/social-network-analysis-for-behavioral-
scientists-snar01/

Delivered by Prof. James Curley

3rd July 2018 - 6th July 2018

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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 – Classes from 09:00 to 17:00

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 – Classes from 09:00 to 17:00

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 – Classes from 09:00 to 17:00

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 – Classes from 09:00 to 17:00

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’.

Send inquiries to oliverhooker@psstatistics.com

1.    November 27th – December 1st 2017
INTRODUCTION TO PYTHON FOR BIOLOGISTS #IPYB
Margam Discovery Centre, Wales, Dr. Martin Jones
http://www.prinformatics.com/course/introduction-to-python-for-biologists-
ipyb04/
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2.    December 4th - 8th 2017
ADVANCING IN STATISTICAL MODELLING USING R #ADVR
Margam Discovery Centre, Wales, Dr. Luc Bussiere, Dr. Tom Houslay, Dr. Ane
Timenes Laugen,
http://www.prstatistics.com/course/advancing-statistical-modelling-using-r-
advr07/
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3.    January 29t – February 2nd 2018
INTRODUCTION TO BAYESIAN HIERARCHICAL MODELLING #IBHM
SCENE, Scotland, Dr. Andrew Parnell
http://www.prstatistics.com/course/introduction-to-bayesian-hierarchical-
modelling-using-r-ibhm02/

4.    January 29th – February 2nd 2018
PHYLOGENETIC DATA ANALYSIS USING R #PHYL
SCENE, Scotland, Dr. Emmanuel Paradis
https://www.prstatistics.com/course/introduction-to-phylogenetic-analysis-
with-r-phyg-phyl02/
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5.    February 19th – 23rd 2018
MOVEMENT ECOLOGY #MOVE
Margam Discovery Centre, Wales, Dr Luca Borger, Dr Ronny Wilson, Dr
Jonathan Potts
https://www.prstatistics.com/course/movement-ecology-move01/

6.    February 19th – 23rd 2018
GEOMETRIC MORPHOMETRICS USING R #GMMR
Margam Discovery Centre, Wales, Prof. Dean Adams, Prof. Michael Collyer,
Dr. Antigoni Kaliontzopoulou
http://www.prstatistics.com/course/geometric-morphometrics-using-r-gmmr01/
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7.    March 5th - 9th 2018
SPATIAL PRIORITIZATION USING MARXAN #MRXN
Margam Discovery Centre, Wales, Jennifer McGowan
https://www.prstatistics.com/course/introduction-to-marxan-mrxn01/

8.    March 12th - 16th 2018
ECOLOGICAL NICHE MODELLING USING R #ENMR
Glasgow, Scotland, Dr. Neftali Sillero
http://www.prstatistics.com/course/ecological-niche-modelling-using-r-
enmr02/

9.    March 19th – 23rd 2018
BEHAVIOURAL DATA ANALYSIS USING MAXIMUM LIKLIHOOD IN R #BDML
Glasgow, Scotland, Dr William Hoppitt
http://www.psstatistics.com/course/behavioural-data-analysis-using-maximum-
likelihood-bdml01/
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10.    April 9th – 13th 2018
NETWORK ANAYLSIS FOR ECOLOGISTS USING R #NTWA
Glasgow, Scotland, Dr. Marco Scotti
https://www.prstatistics.com/course/network-analysis-ecologists-ntwa02/

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

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

13.    April 30th – 4th May 2018
QUANTITATIVE GEOGRAPHIC ECOLOGY: MODELING GENOMES, NICHES, AND COMMUNITIES
#QGER
Glasgow, Scotland, Dr. Dan Warren, Dr. Matt Fitzpatrick
https://www.prstatistics.com/course/quantitative-geographic-ecology-using-r-
modelling-genomes-niches-and-communities-qger01/
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14.    May 7th – 11th 2018 ADVANCES IN MULTIVARIATE ANALYSIS OF SPATIAL
ECOLOGICAL DATA USING R #MVSP
CANADA (QUEBEC), Prof. Pierre Legendre, Dr. Guillaume Blanchet
https://www.prstatistics.com/course/advances-in-spatial-analysis-of-
multivariate-ecological-data-theory-and-practice-mvsp03/

15.    May 14th - 18th 2018
INTRODUCTION TO MIXED (HIERARCHICAL) MODELS FOR BIOLOGISTS #IMBR
CANADA (QUEBEC), Prof Subhash Lele
https://www.prstatistics.com/course/introduction-to-mixed-hierarchical-
models-for-biologists-using-r-imbr01/

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

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

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

19.    May 28th – June 1st 2018
ADVANCED PYTHON FOR BIOLOGISTS #APYB
SCENE, Scotland, Dr. Martin Jones
https://www.prinformatics.com/course/advanced-python-biologists-apyb02/
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20.    June 12th -0 15th 2018
SPECIES DISTRIBUTION MODELLING #DBMR
Myuna Bay sport and recreation, Australia, TBC
COMING SOON www.PRstatistics.com

21.    November 6th – 10th 2017
LANDSCAPE GENETIC DATA ANALYSIS USING R #LNDG
Myuna Bay sport and recreation, Australia, TBC
COMING SOON www.PRstatistics.com

22.    June 18th – 22nd 2018
STRUCTURAL EQUATION MODELLING FOR ECOLOGISTS AND EVOLUTIONARY BIOLOGISTS
USING R #SEMR
Myuna Bay sport and recreation, Australia, TBC
COMING SOON www.PRstatistics.com
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23.    July 2nd - 5th 2018
SOCIAL NETWORK ANALYSIS FOR BEHAVIOURAL SCIENTISTS USING R #SNAR
Glasgow, Scotland, Prof James Curley
http://www.psstatistics.com/course/social-network-analysis-for-behavioral-
scientists-snar01/

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

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

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