Network:

OR

There is also a Public WIFI Network in Downtown Montréal:

Everything you ever wanted to know about network statistics but were afraid to ask (3-hour)
**Organizer:** Momin Malik

Introductions to social network analysis, when talking about how we move beyond collecting, handling, and describing network data to do statistical modeling, often have some version of the statement: "the 'usual' statistics don't work for networks because of dependencies. So, we need specialized models." But what, exactly, do these dependencies do? How do network models address these problems?

Introductions to social network analysis, when talking about how we move beyond collecting, handling, and describing network data to do statistical modeling, often have some version of the statement: "the 'usual' statistics don't work for networks because of dependencies. So, we need specialized models." But what, exactly, do these dependencies do? How do network models address these problems?

Tuesday June 18, 2019 08:30 - 11:30 EDT

DS-R520

DS-R520

Introduction to SNA descriptive statistics and hypothesis testing using R/statnet (3-hour)
**Organizer:** Lorien Jasny

This workshop will serve as an introduction to the use of basic statistical methods for network analysis within the R/statnet platform. The approach taken is practical rather than theoretical, with emphasis on simple, robust methods for hypothesis testing and exploratory data analysis of single and multi-network data sets. Topics will include: tests for marginal relationships between node or graph-level indices and covariates; Monte Carlo tests for structural biases; network correlation, autocorrelation, and regression; and exploratory multivariate analysis of multinetwork data sets. We will also cover interpreting R code in existing functions and writing your own functions. Attendees are expected to have had some prior exposure to R, but extensive experience is not assumed. Completion of the "Introduction to Network Analysis with R and statnet" workshop session is suggested (but not required) as preparation for this session. Familiarity with the basic concepts of descriptive network analysis (e.g., centrality scores, network visualization) is strongly recommended. To get the most out of the workshop, participants are recommended to bring a laptop with R, RStudio, and Statnet installed. Sample data will be provided by the organizer.

This workshop will serve as an introduction to the use of basic statistical methods for network analysis within the R/statnet platform. The approach taken is practical rather than theoretical, with emphasis on simple, robust methods for hypothesis testing and exploratory data analysis of single and multi-network data sets. Topics will include: tests for marginal relationships between node or graph-level indices and covariates; Monte Carlo tests for structural biases; network correlation, autocorrelation, and regression; and exploratory multivariate analysis of multinetwork data sets. We will also cover interpreting R code in existing functions and writing your own functions. Attendees are expected to have had some prior exposure to R, but extensive experience is not assumed. Completion of the "Introduction to Network Analysis with R and statnet" workshop session is suggested (but not required) as preparation for this session. Familiarity with the basic concepts of descriptive network analysis (e.g., centrality scores, network visualization) is strongly recommended. To get the most out of the workshop, participants are recommended to bring a laptop with R, RStudio, and Statnet installed. Sample data will be provided by the organizer.

Tuesday June 18, 2019 08:30 - 11:30 EDT

DS-1525

DS-1525

A Hands-On Introduction to Analyzing Social Networks with UCINET & Netdraw (6-hour)
**Organizers:**

Dan Halgin

Rich DeJordy

This interactive workshop gives all participants an opportunity for hands-on experience analyzing network data using the UCINET/Netdraw software package. We will provide a beginner’s tutorial on the concepts, methods, and data analysis techniques for a whole social network research project, from data entry through reporting results. Together, we will use sample datasets to focus on the interpretation and calculation of some of the most common measures of network analysis at the node, dyad, and whole-network level of analysis. We will also provide a hands-on tutorial for NetDraw, which creates network visualizations.

Dan Halgin

Rich DeJordy

This interactive workshop gives all participants an opportunity for hands-on experience analyzing network data using the UCINET/Netdraw software package. We will provide a beginner’s tutorial on the concepts, methods, and data analysis techniques for a whole social network research project, from data entry through reporting results. Together, we will use sample datasets to focus on the interpretation and calculation of some of the most common measures of network analysis at the node, dyad, and whole-network level of analysis. We will also provide a hands-on tutorial for NetDraw, which creates network visualizations.

Tuesday June 18, 2019 08:30 - 15:00 EDT

DS-1545

DS-1545

Analysis of bibliographic networks (6-hour)
**Organizers:**

Vladimir Batagelj

Daria Maltseva

During our workshop we will present the network approach to bibliographic data and different methods used for their analysis, covering the questions of getting and preparing networks. Among others, we will present a measure of collaborativeness of authors with respect to a given bibliography and show how to compute the network of citations betwenn authors and identify citation communities. The participants will be able to collect bibliographic data, construct the corresponding networks and apply the discussed techniques of network analysis to them. We will use a program Pajek, supported by some special programs in Python and R. Workshop materials will be available at GitHub: https://github.com/bavla/BibNets.

Vladimir Batagelj

Daria Maltseva

During our workshop we will present the network approach to bibliographic data and different methods used for their analysis, covering the questions of getting and preparing networks. Among others, we will present a measure of collaborativeness of authors with respect to a given bibliography and show how to compute the network of citations betwenn authors and identify citation communities. The participants will be able to collect bibliographic data, construct the corresponding networks and apply the discussed techniques of network analysis to them. We will use a program Pajek, supported by some special programs in Python and R. Workshop materials will be available at GitHub: https://github.com/bavla/BibNets.

Tuesday June 18, 2019 08:30 - 15:00 EDT

R-R120

R-R120

Designing and Conducting Online Lab Experiments on Social Networks (6-hour)
**Organizers:**

Jason Radford

David Lazer

The internet enables researchers to perform a wide variety of lab experiments on social networks online. However, conducting online network experiments presents a unique set of challenges ranging from subject attrition and pilot testing large, multi-person studies to recruiting large numbers of subjects to participate together. In the first session, attendees will learn about the recent history of large-scale network experiments and learn the best practices for conducting network experiments online. We will cover the core methodological choices including the design choices that drive success and failure, the strengths and weakness of various subject pools, and techniques for recruiting bursts of subjects. In the second session, attendees will build their own network experiments using HTML, CSS, and JavaScript in the Volunteer Science platform. A basic grasp of these languages is prerequisite, but an introductory training is available upon request to attendees who are unfamiliar with them before the workshop.

Jason Radford

David Lazer

The internet enables researchers to perform a wide variety of lab experiments on social networks online. However, conducting online network experiments presents a unique set of challenges ranging from subject attrition and pilot testing large, multi-person studies to recruiting large numbers of subjects to participate together. In the first session, attendees will learn about the recent history of large-scale network experiments and learn the best practices for conducting network experiments online. We will cover the core methodological choices including the design choices that drive success and failure, the strengths and weakness of various subject pools, and techniques for recruiting bursts of subjects. In the second session, attendees will build their own network experiments using HTML, CSS, and JavaScript in the Volunteer Science platform. A basic grasp of these languages is prerequisite, but an introductory training is available upon request to attendees who are unfamiliar with them before the workshop.

Tuesday June 18, 2019 08:30 - 15:00 EDT

DS-M560

DS-M560

EgoWeb 2.0: Flexible and user friendly social network data collection software (Basic and Advanced) (6-hour)
**Organizers:**

David Kennedy

Marie R. Kennedy, Stacey Giroux

In these hands-on workshops, attendees will learn to use EgoWeb 2.0, an open-source and freely available software for network data collection instrument development, network interview administration, and network data processing and analysis for a variety of data collection modes. Attendees will learn to create data collection instruments that can be administered on laptops, mobile tablets, or over the internet. Workshop attendees will learn how to use EgoWeb 2.0 to collect egocentric/personal network data (Session 1) as well as whole/cognitive network data (Session 2). Session 2 will build off of Session 1 instruction but current users of EgoWeb 2.0 will be able to participate in Session 2 without participating in Session 1.

David Kennedy

Marie R. Kennedy, Stacey Giroux

In these hands-on workshops, attendees will learn to use EgoWeb 2.0, an open-source and freely available software for network data collection instrument development, network interview administration, and network data processing and analysis for a variety of data collection modes. Attendees will learn to create data collection instruments that can be administered on laptops, mobile tablets, or over the internet. Workshop attendees will learn how to use EgoWeb 2.0 to collect egocentric/personal network data (Session 1) as well as whole/cognitive network data (Session 2). Session 2 will build off of Session 1 instruction but current users of EgoWeb 2.0 will be able to participate in Session 2 without participating in Session 1.

Tuesday June 18, 2019 08:30 - 15:00 EDT

DS-2505

DS-2505

From Texts to Networks to Maps: Social Media and Beyond (6-hour)
**Organizers:**

Kathleen Carley

Rick Carley

This workshop teaches participants how to extract networks from texts (e.g. tweets, blogs, email contents, newspapers), analyze and visualize these as networks, and examine the results spatially. Participants will learn how to use NetMapper and ORA. Key issues for network based rhetorical assessment of communicative power, social influence and information roles of actors and social media analytics will be addressed. Data sets to be used will include sample news and twitter data. Semantic and meta-networks (high dimensional networks) will be extracted from the texts using NetMapper. Then these networks will be analyzed and visualized using ORA. Sentiment and stance will be extracted from the texts and analyzed in ORA. Special network metrics for social media analytics will be defined and used to assess the data. Network metrics for social media analytics to identify and cluster actors of interest, identification of topic-groups, echo-chambers, and assessment of texts in terms of communicative power will be discussed as will their use with weighted, valenced data. Finally, those networks with spatial information will be analyzed visualized and assessed using geo-spatial networks. This session is intended to be hands-on.

Kathleen Carley

Rick Carley

This workshop teaches participants how to extract networks from texts (e.g. tweets, blogs, email contents, newspapers), analyze and visualize these as networks, and examine the results spatially. Participants will learn how to use NetMapper and ORA. Key issues for network based rhetorical assessment of communicative power, social influence and information roles of actors and social media analytics will be addressed. Data sets to be used will include sample news and twitter data. Semantic and meta-networks (high dimensional networks) will be extracted from the texts using NetMapper. Then these networks will be analyzed and visualized using ORA. Sentiment and stance will be extracted from the texts and analyzed in ORA. Special network metrics for social media analytics will be defined and used to assess the data. Network metrics for social media analytics to identify and cluster actors of interest, identification of topic-groups, echo-chambers, and assessment of texts in terms of communicative power will be discussed as will their use with weighted, valenced data. Finally, those networks with spatial information will be analyzed visualized and assessed using geo-spatial networks. This session is intended to be hands-on.

Tuesday June 18, 2019 08:30 - 15:00 EDT

DS-R510

DS-R510

Intermediate Social Network Analysis with UCINET (6-hour)
**Organizers:**

Steve Borgatti

Martin Everett

This is a 1-day workshop for participants who already have some experience with network analysis, but would like to learn more. We cover advanced aspects of centrality, finding subgroups, and measuring equivalence. We also cover techniques for measuring network change and handling multiple relations, missing data, non-symmetric data, valued data and 2-mode data. Throughout, we demonstrate powerful, sometimes undocumented, features of UCINET and NETDRAW, including newer routines that make work easier. Note: what makes this workshop advanced is the selection of topics, not the speed or complexity of the exposition. In other words, wherever practical, all concepts are explained from first principles, making as few assumptions about prior knowledge as possible. However, we do assume basic familiarity with UCINET as a pre-requisite for the workshop as given in the introductory workshop.

Steve Borgatti

Martin Everett

This is a 1-day workshop for participants who already have some experience with network analysis, but would like to learn more. We cover advanced aspects of centrality, finding subgroups, and measuring equivalence. We also cover techniques for measuring network change and handling multiple relations, missing data, non-symmetric data, valued data and 2-mode data. Throughout, we demonstrate powerful, sometimes undocumented, features of UCINET and NETDRAW, including newer routines that make work easier. Note: what makes this workshop advanced is the selection of topics, not the speed or complexity of the exposition. In other words, wherever practical, all concepts are explained from first principles, making as few assumptions about prior knowledge as possible. However, we do assume basic familiarity with UCINET as a pre-requisite for the workshop as given in the introductory workshop.

Tuesday June 18, 2019 08:30 - 15:00 EDT

DS-1580

DS-1580

Mixed Methods Research in Social Networks (6-hour)
**Organizers:**

Elisa Belloti

Betina Hollstein

The workshop focuses on the use of mixed methods research designs when studying whole and ego-centered social networks. The workshop will be conducted in two parts. The first part introduces social network qualitative research and the principles of mixed methods research designs and its contributions to the study of social networks, pointing out advantages and challenges of this approach. Illustrations of the theoretical and methodological aspects are given by bringing examples from a variety of fields of research. The second part is devoted to the presentation of concrete procedures to apply mixed methods in network research both at the level of data collection and analysis. This part includes an introduction of different approaches to the collection of whole and ego-centered network data, i.e. interviews, ethnographic methods, archival data, together with visual instruments. It then moves to the analysis of the quantitative and qualitative dimensions of network relationships and structures in a mixed method perspective.

Elisa Belloti

Betina Hollstein

The workshop focuses on the use of mixed methods research designs when studying whole and ego-centered social networks. The workshop will be conducted in two parts. The first part introduces social network qualitative research and the principles of mixed methods research designs and its contributions to the study of social networks, pointing out advantages and challenges of this approach. Illustrations of the theoretical and methodological aspects are given by bringing examples from a variety of fields of research. The second part is devoted to the presentation of concrete procedures to apply mixed methods in network research both at the level of data collection and analysis. This part includes an introduction of different approaches to the collection of whole and ego-centered network data, i.e. interviews, ethnographic methods, archival data, together with visual instruments. It then moves to the analysis of the quantitative and qualitative dimensions of network relationships and structures in a mixed method perspective.

Tuesday June 18, 2019 08:30 - 15:00 EDT

DS-1520

DS-1520

Network Canvas: Simplifying complex network data collection (6-hour)
**Organizers:**

Michelle Birkett

Gregory Lee Phillips, Kate Banner, Bernie Hogan, Joshua Melville, Patrick Francis Janulis

Network Canvas is a free, open-source suite of tools for simplifying the collection of complex network data. It captures data about both the individual and their social network through touch-optimized interfaces, in an interview-assisted environment. Since we represent abstract relationships and attributes visually, complex structural data becomes more tangible and simple to capture.

Michelle Birkett

Gregory Lee Phillips, Kate Banner, Bernie Hogan, Joshua Melville, Patrick Francis Janulis

Network Canvas is a free, open-source suite of tools for simplifying the collection of complex network data. It captures data about both the individual and their social network through touch-optimized interfaces, in an interview-assisted environment. Since we represent abstract relationships and attributes visually, complex structural data becomes more tangible and simple to capture.

Tuesday June 18, 2019 08:30 - 15:00 EDT

DS-M320

DS-M320

Simplifying ego-centered network analysis in R with egor (6-hour)
**Organizer**: Till Krenz

The workshop focuses the analysis of ego-centered networks in R. The first part of the workshop will introduce SNA measures at the alter level (e.g. multiplexity, EI-Index (network subgroups) and the network level (size, density, EI-Index (ego), diversity, proportions of ties with specific attributes). Afterwards we go on with multivariate analyses, both on the network level and the alter level (Regression, Multi-Level Regression, Cluster-Analysis, Clustered Graphs).

Major parts of the workshop will be “hands-on”, utilizing R (R-Studio).

The workshop focuses the analysis of ego-centered networks in R. The first part of the workshop will introduce SNA measures at the alter level (e.g. multiplexity, EI-Index (network subgroups) and the network level (size, density, EI-Index (ego), diversity, proportions of ties with specific attributes). Afterwards we go on with multivariate analyses, both on the network level and the alter level (Regression, Multi-Level Regression, Cluster-Analysis, Clustered Graphs).

Major parts of the workshop will be “hands-on”, utilizing R (R-Studio).

Tuesday June 18, 2019 08:30 - 15:00 EDT

DS-M280

DS-M280

Using R and igraph for Social Network Analysis (6-hour)
**Organizer:** Michal Bojanowski

The workshop introduces R and package igraph for social network data manipulation, visualization, and analysis. The workshop introduces R and package igraph for social network data manipulation, visualization, and analysis.

The material will cover:

-Brief introduction to R.

-Creating and manipulating network data objects.

-Working with node and tie attributes.

-Creating network visualizations.

- A tour through computing selected SNA methods including: degree distribution, centrality measures, shortest paths, connected components, quantifying homophily / segregation, network community detection.

- Connections to other R packages for SNA, e.g.: statnet, RSiena, egonetR.

The workshop introduces R and package igraph for social network data manipulation, visualization, and analysis. The workshop introduces R and package igraph for social network data manipulation, visualization, and analysis.

The material will cover:

-Brief introduction to R.

-Creating and manipulating network data objects.

-Working with node and tie attributes.

-Creating network visualizations.

- A tour through computing selected SNA methods including: degree distribution, centrality measures, shortest paths, connected components, quantifying homophily / segregation, network community detection.

- Connections to other R packages for SNA, e.g.: statnet, RSiena, egonetR.

Tuesday June 18, 2019 08:30 - 15:00 EDT

DS-1540

DS-1540

Introduction to Egocentric Network Data Analysis with ERGMs using statnet (3-hour)
**Organizers:**

Pavel Krivitsky

Martina Morris

This workshop will provide an introduction to analyzing egocentrically sampled data with exponential-family random graph models (ERGMs) for statistical network analysis. It will be a hands-on workshop demonstrating how to fit, diagnose and simulate both static and dynamic ERG models from such data, using the 'ergm.ego' package, part of the integrated statnet software collection in R. Topics covered in this session include: a review of approaches to analyzing egocentrically sampled data, an overview of the statistical theory that supports the use of ERGMs for egocentric samples; defining and fitting ERGMs to egocentric data; interpretation of model coefficients; goodness-of-fit and model adequacy checking; and simulation of complete networks from the specified ERG models. statnet is an open source collection of integrated packages for the R statistical computing environment that support the representation, manipulation, visualization, modeling, simulation, and analysis of network data.

Pavel Krivitsky

Martina Morris

This workshop will provide an introduction to analyzing egocentrically sampled data with exponential-family random graph models (ERGMs) for statistical network analysis. It will be a hands-on workshop demonstrating how to fit, diagnose and simulate both static and dynamic ERG models from such data, using the 'ergm.ego' package, part of the integrated statnet software collection in R. Topics covered in this session include: a review of approaches to analyzing egocentrically sampled data, an overview of the statistical theory that supports the use of ERGMs for egocentric samples; defining and fitting ERGMs to egocentric data; interpretation of model coefficients; goodness-of-fit and model adequacy checking; and simulation of complete networks from the specified ERG models. statnet is an open source collection of integrated packages for the R statistical computing environment that support the representation, manipulation, visualization, modeling, simulation, and analysis of network data.

Tuesday June 18, 2019 12:00 - 15:00 EDT

DS-R520

DS-R520

Moving beyond Descriptives: Basic Network Statistics with R/statnet (3-hour)
**Organizer:** Lorien Jasny

This workshop will serve as an introduction to the use of basic statistical methods for network analysis within the R/statnet platform. The approach taken is practical rather than theoretical, with emphasis on simple, robust methods for hypothesis testing and exploratory data analysis of single and multi-network data sets. Topics will include: tests for marginal relationships between node or graph-level indices and covariates; Monte Carlo tests for structural biases; network correlation, autocorrelation, and regression; and exploratory multivariate analysis of multinetwork data sets. We will also cover interpreting R code in existing functions and writing your own functions. Attendees are expected to have had some prior exposure to R, but extensive experience is not assumed. Completion of the “Introduction to Network Analysis with R and statnet” workshop session is suggested (but not required) as preparation for this session. Familiarity with the basic concepts of descriptive network analysis (e.g., centrality scores, network visualization) is strongly recommended. To get the most out of the workshop, participants are recommended to bring a laptop with R, RStudio, and statnet installed. Sample data and code will be provided by the organizer.

This workshop will serve as an introduction to the use of basic statistical methods for network analysis within the R/statnet platform. The approach taken is practical rather than theoretical, with emphasis on simple, robust methods for hypothesis testing and exploratory data analysis of single and multi-network data sets. Topics will include: tests for marginal relationships between node or graph-level indices and covariates; Monte Carlo tests for structural biases; network correlation, autocorrelation, and regression; and exploratory multivariate analysis of multinetwork data sets. We will also cover interpreting R code in existing functions and writing your own functions. Attendees are expected to have had some prior exposure to R, but extensive experience is not assumed. Completion of the “Introduction to Network Analysis with R and statnet” workshop session is suggested (but not required) as preparation for this session. Familiarity with the basic concepts of descriptive network analysis (e.g., centrality scores, network visualization) is strongly recommended. To get the most out of the workshop, participants are recommended to bring a laptop with R, RStudio, and statnet installed. Sample data and code will be provided by the organizer.

Tuesday June 18, 2019 12:00 - 15:00 EDT

DS-1525

DS-1525

Introduction to egocentric network analysis with R (6-hour)
**Organizer:** Raffaele Vacca

This workshop is an introduction to the R programming language for statistical computing, and the tools it offers to represent, store and manipulate egocentric network data; to visualize egonetworks; and to conduct compositional and structural analysis on large collections of egonetworks. No previous familiarity with R is required. Topics include: Short introduction to egonetwork research and data; introduction to data structures and network objects in R; visualizing ego-networks; calculating summary measures on ego-network composition and structure; converting your ego-network measures to general R functions; applying your functions to many ego-networks in few lines of code; "split-apply-combine" with the tidyverse suite of packages. We'll cover both base R functions and specific packages, including igraph, network (from statnet), dplyr and purrr (from tidyverse). This workshop has been taught for the past five years at several network analysis conferences, including INSNA's Sunbelt and EUSN conferences. At the INSNA Sunbelt, it can be taken as a follow-up to Michał Bojanowski's igraph workshop, and as an introduction to Till Krenz's workshop on the egor package.

This workshop is an introduction to the R programming language for statistical computing, and the tools it offers to represent, store and manipulate egocentric network data; to visualize egonetworks; and to conduct compositional and structural analysis on large collections of egonetworks. No previous familiarity with R is required. Topics include: Short introduction to egonetwork research and data; introduction to data structures and network objects in R; visualizing ego-networks; calculating summary measures on ego-network composition and structure; converting your ego-network measures to general R functions; applying your functions to many ego-networks in few lines of code; "split-apply-combine" with the tidyverse suite of packages. We'll cover both base R functions and specific packages, including igraph, network (from statnet), dplyr and purrr (from tidyverse). This workshop has been taught for the past five years at several network analysis conferences, including INSNA's Sunbelt and EUSN conferences. At the INSNA Sunbelt, it can be taken as a follow-up to Michał Bojanowski's igraph workshop, and as an introduction to Till Krenz's workshop on the egor package.

Tuesday June 18, 2019 12:00 - 18:00 EDT

DS-1570

DS-1570

An Introduction to Necessary Condition Analysis (NCA) (3-hour)
**Organizers:**

Zsofia Toth

Jan Dul

This workshop will provide an introduction to Necessary Condition Analysis (NCA). NCA is an upcoming approach that can be used to test whether a condition (X) is necessary but not sufficient for an outcome (Y). It provides a new perspective on existing phenomena and is complementary to the regression-based modeling that we usually apply.

Zsofia Toth

Jan Dul

This workshop will provide an introduction to Necessary Condition Analysis (NCA). NCA is an upcoming approach that can be used to test whether a condition (X) is necessary but not sufficient for an outcome (Y). It provides a new perspective on existing phenomena and is complementary to the regression-based modeling that we usually apply.

Tuesday June 18, 2019 15:00 - 18:00 EDT

DS-M320

DS-M320

Analysis of Multiplex Social Networks with R (3-hour)
**Organizers:**

Matteo Magnani

Luca Rossi

A multiplex network is a network where actors are connected through different types of edges, such as "working together", "friend", etc. These different types of connections are also known as layers. The workshop will introduce the R multinet library for the analysis of multiplex social networks. For each topic, a quick presentation of the relevant theory and methods will be followed by a practical application on a real pedagogical dataset. The main topics covered will be: network exploration, actor measures (degree, neighborhood, ...), layer-dependent actor measures (layer relevance, ...), layer comparison methods, community detection (generalized louvain, clique percolation, ...), and a quick discussion of generative models for multiplex networks. Part of the presented material is covered in the book "Multilayer Social Networks", Cambridge, 2016. The workshop includes methods developed in different fields by several different authors.

Matteo Magnani

Luca Rossi

A multiplex network is a network where actors are connected through different types of edges, such as "working together", "friend", etc. These different types of connections are also known as layers. The workshop will introduce the R multinet library for the analysis of multiplex social networks. For each topic, a quick presentation of the relevant theory and methods will be followed by a practical application on a real pedagogical dataset. The main topics covered will be: network exploration, actor measures (degree, neighborhood, ...), layer-dependent actor measures (layer relevance, ...), layer comparison methods, community detection (generalized louvain, clique percolation, ...), and a quick discussion of generative models for multiplex networks. Part of the presented material is covered in the book "Multilayer Social Networks", Cambridge, 2016. The workshop includes methods developed in different fields by several different authors.

Tuesday June 18, 2019 15:00 - 18:00 EDT

DS-1540

DS-1540

Enso: Engaging Social Network Data Collection Software (3-hour)
**Organizer:** Kate Eddens

Enso is an open-source software application for collecting sociometric (complete, roster-based) and egocentric (personal) network data. Designed to be engaging and playful, Enso can be used on computers or mobile devices with or without an internet connection, making it ready for field collection on tablets or phones.

This workshop will walk participants through developing a network data collection survey instrument in Enso, administering the survey, and exporting survey data in CSV format. Participants can bring their own laptops to access a server instance of Enso through a web browser and participate in hands-on survey development and administration exercises. If participants do not have a laptop to bring, they may still attend and choose to follow along with the workshop instructor.

Enso is an open-source software application for collecting sociometric (complete, roster-based) and egocentric (personal) network data. Designed to be engaging and playful, Enso can be used on computers or mobile devices with or without an internet connection, making it ready for field collection on tablets or phones.

This workshop will walk participants through developing a network data collection survey instrument in Enso, administering the survey, and exporting survey data in CSV format. Participants can bring their own laptops to access a server instance of Enso through a web browser and participate in hands-on survey development and administration exercises. If participants do not have a laptop to bring, they may still attend and choose to follow along with the workshop instructor.

Tuesday June 18, 2019 15:00 - 18:00 EDT

DS-2505

DS-2505

Exploring networks using latent variable models in R with lvm4net (3-hour)
**Organizer:** Isabella Gollini

Latent variable network models represent an effective and efficient approach for exploring the structure of complex relational data. In this hands-on workshop we will describe and demonstrate the modelling approaches of the lvm4net package for R by the analysis of real network data. This package have been developed to provide a rich source of insights on probabilistic visualisation and clustering and describing the heterogenous connectivity structure of one-mode, two-mode, and multiplex networks using fast estimation techniques (such as variational inference).

Latent variable network models represent an effective and efficient approach for exploring the structure of complex relational data. In this hands-on workshop we will describe and demonstrate the modelling approaches of the lvm4net package for R by the analysis of real network data. This package have been developed to provide a rich source of insights on probabilistic visualisation and clustering and describing the heterogenous connectivity structure of one-mode, two-mode, and multiplex networks using fast estimation techniques (such as variational inference).

Tuesday June 18, 2019 15:00 - 18:00 EDT

DS-M560

DS-M560

Introduction to ERGMs using statnet (3-hour)
**Organizers:**

David Hunter

Steven M Goodreau

This workshop will provide a hands-on tutorial to using exponential-family random graph models (ERGMs) for statistical analysis of social networks, using the "ergm" package in statnet. The ergm package provides tools for the specification, estimation, assessment and simulation of ERGMs that incorporate the complex dependencies within networks. Topics covered in this workshop include: an overview of the ERGM framework; defining and fitting models to empirical data; interpretation of model coefficients; goodness-of-fit and model adequacy checking; simulation of networks using ERG models; degeneracy assessment and avoidance; and modeling and simulation of complete networks from egocentrically sampled data. statnet is an open source collection of integrated packages for the R statistical computing environment that support the representation, manipulation, visualization, modeling, simulation, and analysis of network data.

David Hunter

Steven M Goodreau

This workshop will provide a hands-on tutorial to using exponential-family random graph models (ERGMs) for statistical analysis of social networks, using the "ergm" package in statnet. The ergm package provides tools for the specification, estimation, assessment and simulation of ERGMs that incorporate the complex dependencies within networks. Topics covered in this workshop include: an overview of the ERGM framework; defining and fitting models to empirical data; interpretation of model coefficients; goodness-of-fit and model adequacy checking; simulation of networks using ERG models; degeneracy assessment and avoidance; and modeling and simulation of complete networks from egocentrically sampled data. statnet is an open source collection of integrated packages for the R statistical computing environment that support the representation, manipulation, visualization, modeling, simulation, and analysis of network data.

Tuesday June 18, 2019 15:00 - 18:00 EDT

DS-R520

DS-R520

Introduction to Social Network Data Collection with an Emphasis on Social Survey Methods (3-hour)
**Organizer: **David Tindall

This workshop is intended for relative newcomers to social network analysis. The workshop will provide an introduction to social network data collection with an emphasis on social survey methods. The workshop will consider a variety of related methodological issues such as research design, measurement, sampling, data analysis, and ethics, as well as the linkage of these issues to data collection. Different types of data collection techniques will be illustrated such as the name generator, position generator, and name roster. The different opportunities and constraints associated with data collection for whole versus ego-networks will be considered. Some discussion of non-survey techniques may also be provided. Some attention may also be given to mixed methods.

This workshop is intended for relative newcomers to social network analysis. The workshop will provide an introduction to social network data collection with an emphasis on social survey methods. The workshop will consider a variety of related methodological issues such as research design, measurement, sampling, data analysis, and ethics, as well as the linkage of these issues to data collection. Different types of data collection techniques will be illustrated such as the name generator, position generator, and name roster. The different opportunities and constraints associated with data collection for whole versus ego-networks will be considered. Some discussion of non-survey techniques may also be provided. Some attention may also be given to mixed methods.

Tuesday June 18, 2019 15:00 - 18:00 EDT

DS-1520

DS-1520

Multilevel Modeling for Egocentric Network Analysis (3-hour)
**Organizer:** Brea Perry

This workshop will provide an overview of multilevel modeling (MLM) techniques for analyzing egocentric network data, where alters are nested in ego networks. Multilevel modeling offers a number of important advantages over standard aggregation and regression techniques for egocentric analysis, including taking full advantage of variation across alters, increased statistical power, and the ability to test complex research questions that cross levels of analysis. In this workshop, participants will learn when it is appropriate to use MLM for egocentric analysis and how to formulate and test multilevel hypotheses. In addition, the workshop will provide an introduction to the multilevel variance-components model and special considerations for egocentric data. Finally, participants will learn how to run and interpret MLM for egocentric data using the software program Stata and R.

This workshop will provide an overview of multilevel modeling (MLM) techniques for analyzing egocentric network data, where alters are nested in ego networks. Multilevel modeling offers a number of important advantages over standard aggregation and regression techniques for egocentric analysis, including taking full advantage of variation across alters, increased statistical power, and the ability to test complex research questions that cross levels of analysis. In this workshop, participants will learn when it is appropriate to use MLM for egocentric analysis and how to formulate and test multilevel hypotheses. In addition, the workshop will provide an introduction to the multilevel variance-components model and special considerations for egocentric data. Finally, participants will learn how to run and interpret MLM for egocentric data using the software program Stata and R.

Tuesday June 18, 2019 15:00 - 18:00 EDT

DS-1545

DS-1545

Network visualization with R (3-hour)
**Organizer:** Katherine Ognyanova

This workshop will cover network visualization using the R language for statistical computing (cran.r-project.org) and RStudio (rstudio.com). Participants should have some prior knowledge of R and network concepts. The workshop will provide a step-by-step guide describing through series of examples the path from raw data to graph visualization in the igraph and Statnet frameworks. The advanced portion of the workshop will touch on dynamic visualization for longitudinal networks and combining networks with geographic maps. We will also discuss ways of converting graphs in R to interactive JavaScript visualizations for the Web.

This workshop will cover network visualization using the R language for statistical computing (cran.r-project.org) and RStudio (rstudio.com). Participants should have some prior knowledge of R and network concepts. The workshop will provide a step-by-step guide describing through series of examples the path from raw data to graph visualization in the igraph and Statnet frameworks. The advanced portion of the workshop will touch on dynamic visualization for longitudinal networks and combining networks with geographic maps. We will also discuss ways of converting graphs in R to interactive JavaScript visualizations for the Web.

Tuesday June 18, 2019 15:00 - 18:00 EDT

DS-M280

DS-M280

Permutation Tests for Network Data (3-hour)
**Organizer:** David Dekker

This workshop focuses on 3 topics regarding the multiple regression quadratic assignment procedure, which encompasses permutation-based tests for network data in the regression analyses framework. First, the workshop provides an introduction into permutation-based testing by explaining the necessity to deal with structural dependencies when analyzing network-like data and demonstrating the mechanics of permutations of data organized in square matrices. Second, in the workshop we will be elaborating on the use of different permutation scheme's for different tests. F-test for model significance and t-tests for coefficient significance require different permutation approaches. Specifically, the different uses of Y-permutation and DSP-permutation will be clarified. Third, an extension of linear models dealing with exogenous grouping will be presented to the participants. The usefulness of introducing further restictions on permutations to deal with exogenous groupings of ties demonstrates the versatility of the approach. An application with time-series and geographic network data is presented. Participants may expect to feel more proficient in using permutation techniques and encouraged to explore the wide range of possible applications for permutation testing that remain unutilized to this day. Technical requirements involve a basic understanding of correlation and regression analyses, and, access to UCInet and R's statnet.

This workshop focuses on 3 topics regarding the multiple regression quadratic assignment procedure, which encompasses permutation-based tests for network data in the regression analyses framework. First, the workshop provides an introduction into permutation-based testing by explaining the necessity to deal with structural dependencies when analyzing network-like data and demonstrating the mechanics of permutations of data organized in square matrices. Second, in the workshop we will be elaborating on the use of different permutation scheme's for different tests. F-test for model significance and t-tests for coefficient significance require different permutation approaches. Specifically, the different uses of Y-permutation and DSP-permutation will be clarified. Third, an extension of linear models dealing with exogenous grouping will be presented to the participants. The usefulness of introducing further restictions on permutations to deal with exogenous groupings of ties demonstrates the versatility of the approach. An application with time-series and geographic network data is presented. Participants may expect to feel more proficient in using permutation techniques and encouraged to explore the wide range of possible applications for permutation testing that remain unutilized to this day. Technical requirements involve a basic understanding of correlation and regression analyses, and, access to UCInet and R's statnet.

Tuesday June 18, 2019 15:00 - 18:00 EDT

DS-1580

DS-1580

statnetWeb: The easy way to learn (or teach) statistical modeling of network data with ERGMs (3-hour)
**Organizer: **Martina Morris

This workshop will provide a hands-on introduction to new software, statnetWeb, that provides a simple point-and-click interface for the statistical analysis of network data, including ERGMs. It is intended both for newbies to statistical network modeling, and for instructors seeking a robust software application for teaching introductory network analysis courses. Topics covered include: uploading network data, using plots and descriptive statistics to learn about the network, fitting exponential-family random graph models (ERGMs), model diagnostics, goodness of fit, and simulations. statnetWeb allows users to focus on concepts, rather than code. It runs in a web browser window, providing access to the functionality of the statnet suite of R packages without the need to learn R programming or, in some cases, download or install R and statnet. The app can be used as a stand-alone software application, or as a bridge to learn the traditional command-line statnet software in R.

This workshop will provide a hands-on introduction to new software, statnetWeb, that provides a simple point-and-click interface for the statistical analysis of network data, including ERGMs. It is intended both for newbies to statistical network modeling, and for instructors seeking a robust software application for teaching introductory network analysis courses. Topics covered include: uploading network data, using plots and descriptive statistics to learn about the network, fitting exponential-family random graph models (ERGMs), model diagnostics, goodness of fit, and simulations. statnetWeb allows users to focus on concepts, rather than code. It runs in a web browser window, providing access to the functionality of the statnet suite of R packages without the need to learn R programming or, in some cases, download or install R and statnet. The app can be used as a stand-alone software application, or as a bridge to learn the traditional command-line statnet software in R.

Tuesday June 18, 2019 15:00 - 18:00 EDT

R-R120

R-R120

Understanding Diffusion with NetDiffusR (3-hour)
**Organizers:**

Tom Valente

George Vega Yo

The netdiffuseR package provides a set of tools for analyzing and simulating diffusion of innovations and contagion processes on networks. In this workshop we demonstrate the features of the package through the analysis of both empirical and simulated data on the diffusion of innovations. The session will include examples on how to use netdiffuseR jointly with other network analysis packages such as RSiena, statnet, and igraph. NetdiffuseR's main features are computing network exposure models based on various weight matrices (direct ties, structural equivalence, attribute-weighted, etc.), thresholds, infectiousness and susceptibility, among others. The package works with both static and dynamic networks. Some other capabilities include handling relative large graphs, simulating networks and diffusion of innovation processes, and visualizing the diffusion of innovations. While there are no pre-requisites, it is suggested to have a working knowledge of the R programming language.

Tom Valente

George Vega Yo

The netdiffuseR package provides a set of tools for analyzing and simulating diffusion of innovations and contagion processes on networks. In this workshop we demonstrate the features of the package through the analysis of both empirical and simulated data on the diffusion of innovations. The session will include examples on how to use netdiffuseR jointly with other network analysis packages such as RSiena, statnet, and igraph. NetdiffuseR's main features are computing network exposure models based on various weight matrices (direct ties, structural equivalence, attribute-weighted, etc.), thresholds, infectiousness and susceptibility, among others. The package works with both static and dynamic networks. Some other capabilities include handling relative large graphs, simulating networks and diffusion of innovation processes, and visualizing the diffusion of innovations. While there are no pre-requisites, it is suggested to have a working knowledge of the R programming language.

Tuesday June 18, 2019 15:00 - 18:00 EDT

DS-R510

DS-R510

Valued Tie Network Modeling with statnet (3-hour)
**Organizers:**

Pavel Krivitsky

Carter Butts

This workshop provides instruction on how to model social networks with ties that have weights (e.g., counts of interactions) or are ranks (i.e., each actor ranks the others according to some criterion). We will cover the use of latent space models and exponential-family random graph models (ERGMs) generalized to valued ties, emphasizing a hands-on approach to fitting these models to empirical data using the ergm and latentnet packages in statnet. statnet is an open source collection of integrated packages for the R statistical computing environment that support the representation, manipulation, visualization, modeling, simulation, and analysis of network data.

Pavel Krivitsky

Carter Butts

This workshop provides instruction on how to model social networks with ties that have weights (e.g., counts of interactions) or are ranks (i.e., each actor ranks the others according to some criterion). We will cover the use of latent space models and exponential-family random graph models (ERGMs) generalized to valued ties, emphasizing a hands-on approach to fitting these models to empirical data using the ergm and latentnet packages in statnet. statnet is an open source collection of integrated packages for the R statistical computing environment that support the representation, manipulation, visualization, modeling, simulation, and analysis of network data.

Tuesday June 18, 2019 15:00 - 18:00 EDT

DS-1525

DS-1525

Extending ERGM Functionality within statnet: Building Custom User Terms (3-hour)
**Organizers:**

Steven Goodreau

David R Hunter

Have you ever wanted to write your own term for an ERGM model? If so, this is the workshop for you. Exponential-family random graph models (ERGMs) represent a powerful and flexible class of models for the statistical analysis of networks. statnet is a set of packages that implements a wide range of ERGMs in the R computing environment.

The variables on the right hand side of an ERGM equation are different from the covariates in more traditional statistical models because they must be coded up by hand before they can be used in a model. statnet includes about 100 of the most commonly used terms in the ergm package; but if you want a specific term that is not included in the list, you would need to code it up yourself. This workshop will teach participants how to do this.

Steven Goodreau

David R Hunter

Have you ever wanted to write your own term for an ERGM model? If so, this is the workshop for you. Exponential-family random graph models (ERGMs) represent a powerful and flexible class of models for the statistical analysis of networks. statnet is a set of packages that implements a wide range of ERGMs in the R computing environment.

The variables on the right hand side of an ERGM equation are different from the covariates in more traditional statistical models because they must be coded up by hand before they can be used in a model. statnet includes about 100 of the most commonly used terms in the ergm package; but if you want a specific term that is not included in the list, you would need to code it up yourself. This workshop will teach participants how to do this.

Wednesday June 19, 2019 08:30 - 11:30 EDT

DS-1520

DS-1520

Introduction to Modeling Temporal (dynamic) Networks using TERGMs in statnet (3-hour)
**Organizers:**

Martina Morris

Steven M Goodreau

This workshop will provide a hands-on tutorial on the estimation and simulation of dynamic networks with Temporal Exponential-Family Random Graph Models (TERGMs) using the 'tergm' package in statnet. TERGMs can be used for both estimation from and simulation of dynamic network data. The topics covered in this workshop include exploratory data analysis with temporal network data (using the statnet packages tsna for descriptive statistics and ndtv to create network movies), model estimation (from network panel data, a single cross-sectional network with link duration information, and cross-sectional, egocentrically sampled network data), model diagnostics, and simulating dynamic networks from fitted models. These methods can be used with both fixed and changing node sets. statnet is an open source collection of integrated packages for the R statistical computing environment that support the representation, manipulation, visualization, modeling, simulation, and analysis of network data.

Martina Morris

Steven M Goodreau

This workshop will provide a hands-on tutorial on the estimation and simulation of dynamic networks with Temporal Exponential-Family Random Graph Models (TERGMs) using the 'tergm' package in statnet. TERGMs can be used for both estimation from and simulation of dynamic network data. The topics covered in this workshop include exploratory data analysis with temporal network data (using the statnet packages tsna for descriptive statistics and ndtv to create network movies), model estimation (from network panel data, a single cross-sectional network with link duration information, and cross-sectional, egocentrically sampled network data), model diagnostics, and simulating dynamic networks from fitted models. These methods can be used with both fixed and changing node sets. statnet is an open source collection of integrated packages for the R statistical computing environment that support the representation, manipulation, visualization, modeling, simulation, and analysis of network data.

Wednesday June 19, 2019 08:30 - 11:30 EDT

DS-R520

DS-R520

Method selection and adaptation (3-hour)
**Organizer: **Ulrik Brandes

Which centrality measure should I use? Why are there no interesting regular equivalences? Is modularity clustering appropriate for my data? Or should I use this fancy new machine learning approach that everyone keeps recommending? And how do I defend my choices when reviewers disagree?

This workshop will introduce the pivotal notion of network position, i.e., how an actor relates to everyone else in the network, as a means to inform method selection by theory and context. The approach reveals tacit assumptions in commonly used methods, and facilitates adaption without requiring a degree in mathematics.

Which centrality measure should I use? Why are there no interesting regular equivalences? Is modularity clustering appropriate for my data? Or should I use this fancy new machine learning approach that everyone keeps recommending? And how do I defend my choices when reviewers disagree?

This workshop will introduce the pivotal notion of network position, i.e., how an actor relates to everyone else in the network, as a means to inform method selection by theory and context. The approach reveals tacit assumptions in commonly used methods, and facilitates adaption without requiring a degree in mathematics.

Wednesday June 19, 2019 08:30 - 11:30 EDT

DS-1525

DS-1525

Modeling Relational Event Dynamics with R/statnet (3-hour)
**Organizer:** Carter Butts

This workshop will provide an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within R/statnet platform. We will begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We will then discuss estimation of dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Using the informR package, we will then show how to construct models for spell data, and data involving multiple event types.

This workshop will provide an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within R/statnet platform. We will begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We will then discuss estimation of dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Using the informR package, we will then show how to construct models for spell data, and data involving multiple event types.

Wednesday June 19, 2019 08:30 - 11:30 EDT

DS-1545

DS-1545

Relational event models for large and multivariate event networks - introduction to the eventnet software (3-hour)
**Organizers:**

Juergen Lerner

Mark Tranmer, Federica Bianchi

Relational event models are statistical models for social interaction networks such as communication networks, online collaboration, and social media networks that are typically observed with fine-grained time resolution. Sampling techniques originating from the field of survival analysis allow to reliably fit relational event models to networks of millions of nodes connected by hundreds of millions of dyadic events.

This workshop provides a practical introduction to relational event modeling with the event network analyzer (http://algo.uni.kn/software/eventnet/) illustrated on publicly available data from two application domains: (1) interaction events in international relation networks and (2) online collaboration networks in Wikipedia. These networks vary in a wide range of characteristics such as the presence of event signs, weights, or types; node and dyad covariates; one-mode vs two-mode networks; time resolution; and, last but not least, network size.

Juergen Lerner

Mark Tranmer, Federica Bianchi

Relational event models are statistical models for social interaction networks such as communication networks, online collaboration, and social media networks that are typically observed with fine-grained time resolution. Sampling techniques originating from the field of survival analysis allow to reliably fit relational event models to networks of millions of nodes connected by hundreds of millions of dyadic events.

This workshop provides a practical introduction to relational event modeling with the event network analyzer (http://algo.uni.kn/software/eventnet/) illustrated on publicly available data from two application domains: (1) interaction events in international relation networks and (2) online collaboration networks in Wikipedia. These networks vary in a wide range of characteristics such as the presence of event signs, weights, or types; node and dyad covariates; one-mode vs two-mode networks; time resolution; and, last but not least, network size.

Wednesday June 19, 2019 08:30 - 11:30 EDT

DS-1580

DS-1580

Social Network Approaches for Behavior Change (3-hour)
**Organizer: **Tom Valente

This workshop introduces the many ways that social networks influence individual and networklevel behaviors. It also provides a brief introduction to analytic approaches for understanding network influences on behaviors; and reviews existing evidence for the utility of using social network data for behavior change in a variety of settings including health behaviors and organizational performance. The workshop presents a typology of network interventions and reviews existing evidence on the effectiveness of network interventions. (Students familiar with the R environment may follow an R script written to demonstrate the 24 or so tactical interventions presented.)

This workshop introduces the many ways that social networks influence individual and networklevel behaviors. It also provides a brief introduction to analytic approaches for understanding network influences on behaviors; and reviews existing evidence for the utility of using social network data for behavior change in a variety of settings including health behaviors and organizational performance. The workshop presents a typology of network interventions and reviews existing evidence on the effectiveness of network interventions. (Students familiar with the R environment may follow an R script written to demonstrate the 24 or so tactical interventions presented.)

Wednesday June 19, 2019 08:30 - 11:30 EDT

DS-R510

DS-R510