The 8th European Conference on Social Networks (EUSN 2026)

Instructor
Tomáš Diviák, James Hollway, Robert W. Krause, Filip Agneessens

Time
Full day, 11 August, Tuesday

Abstract
In this 6-hour workshop, we introduce the statistical programming language R to network researchers. We focus on several recently developed R packages (manynet, autograph, and xUCINET) that utilise modern and user-friendly code consistent with best practices in learning R more broadly (such as tidyverse principles). All the packages we introduce are free and include tutorials to support further learning.

The workshop covers a range of network types, from one-mode uniplex networks with attributes to more complex forms such as two-mode, multiplex, signed, and longitudinal networks. Using these examples, we guide participants through the key stages of working with network data in R: importing and processing data, conducting and interpreting descriptive analyses, and producing clear and informative visualisations.

Participants are encouraged to bring their laptops, as the workshop includes hands-on exercises throughout.

Instructors
Robert W. Krause, James Hollway, Tomáš Diviák, Filip Agneessens

Time
Half-day, 15 August, Saturday, 8.30-12.00

Abstract
This 3-hour workshop provides an introduction to statistical methods for analysing social networks. The focus is on nodal and dyadic level analysis. We will be using R packages migraph, infernet, sna, and xUCINET to perform these analyses.

Instructors
Alberto Caimo

Time
Half-day, 15 August, Saturday, 14.30-18.00

Abstract
Exponential Random Graph Models (ERGMs) are powerful tools for analyzing complex network data, yet their estimation can be challenging due to issues such as degeneracy, convergence difficulties, and model uncertainty. 
Bayesian methods offer a coherent solution by providing stable inference, clear uncertainty quantification, and principled approaches to model comparison. This workshop introduces participants to Bayesian ERGMs using the Bergm package in R, a user-friendly framework for Bayesian network modeling.

Participants will learn the essential workflow for Bayesian ERGM analysis, including model specification, prior selection, estimation options for large networks, convergence diagnostics, posterior goodness of fit checks and model selection. Through hands-on exercises with real network datasets, the workshop will demonstrate how Bergm can improve ERGM estimation, incorporate prior knowledge, and yield deeper insight into network structure and processes.

Key topics include:

• Foundations of ERGMs and motivations for Bayesian inference
• Using Bergm for model fitting, diagnostics, and interpretation
• Posterior predictive checks and model comparison
• Practical guidance for applying Bayesian ERGMs in research

Designed for researchers and students working with network data, the session assumes basic familiarity with ERGMs but requires no prior experience with Bayesian statistics.

Attendees will leave with practical code examples and the skills needed to confidently apply Bayesian ERGMs using Bergm.

Instructors
Viviana Amati, Marion Hoffman

Time
Full day, 11 August, Tuesday

Abstract
This workshop offers a basic introduction to the theory and application of Stochastic Actor-Oriented Models (SAOMs). SAOMs are a statistical model family developed for the analysis of social network panel data, understood here as two or more repeated observations of a network on a given node set (usually between 20 and a few hundred nodes). The method is implemented in the RSiena package in the R software. 

The first part of the workshop will focus on the intuitive understanding of the model and operation of the software. The second part will present models for the simultaneous dynamics of networks and behavior, and other more advanced topics such as model specification, multivariate networks, and goodness of fit checking.

Course participants should have a basic understanding of social network analysis concepts and methods, and basic knowledge of the R programming language is necessary to successfully follow the workshop. Basic knowledge of multivariate statistical models (e.g., linear regression) is recommended. Participants should bring a laptop to the workshop with the latest versions of R, RStudio (or their preferred GUI if any), and the RSiena R package installed.

Instructors
Cornelius Fritz, Michael Schweinberger

Time
Half-day, 11 August, Tuesday, 9.00-12.30

Abstract
In the interconnected and interdependent world of the twenty-first century, individual and collective outcomes — such as personal and public health, economic welfare, or war and peace — are affected by relationships among individual, corporate, state, and non-state actors. To understand how the world of the twenty-first century operates and make model-based predictions, it is vital to study networks of relationships and gain insight into how the structure of networks affects individual and collective outcomes. To study relationships among attributes under interference, a comprehensive regression framework for dependent predictors, outcomes, and connections is needed. We will provide workshop participants with a hands-on introduction to R package iglm, which implements a comprehensive regression framework for dependent predictors, treatments, outcomes, and connections with important advantages over existing approaches, including interpretability, scalability, and provable theoretical guarantees. We will demonstrate that R package iglm can be used to study spillover in connected populations, including hate speech on social media.

Fritz, C., Schweinberger, M., Bhadra, S., and D.R. Hunter (in press). A regression framework for studying relationships among attributes under network interference. Journal of the American Statistical Association.

A preprint is available at https://www.tandfonline.com/doi/full/10.1080/01621459.2025.2565851?src=exp-la
or https://arxiv.org/abs/2410.07555

Instructor
Marc Schalberger, Cornelius Fritz

Time
Half-day, 11 August, Tuesday, 13.30-17.00

Abstract
Substantive research in the Social Sciences regularly investigates signed networks, in which edges between actors are either positive or negative. One often-studied example within International Relations for this type of network consists of countries that can cooperate with or fight against each other. Other empirical settings in which such data emerge involve trust and distrust, or support and opposition. These tie types coexist and affect each other, so there is a need to model them in a way that standard Exponential Random Graph Models (ERGMs) cannot represent. This workshop introduces methods for modeling such signed networks using the ergm.sign R package.  ergm.sign extends ERGMs and Temporal ERGMs (TERGMs) to handle signed data (yielding Signed Exponential Random Graph Models, SERGM, proposed in Fritz et al., 2025). In this hands-on tutorial, you will learn how to

- Extend ERGMs to signed connections and interpret the estimated model;
- Specify and visualize signed networks in R;
- Fit signed ERGMs in static and dynamic settings that, for instance, enable testing for structural balance;
- Assess model fit through simulations.

The basic ideas of SERGMs will be introduced and demonstrated by examples. Participants will be provided with sample R scripts and lecture slides, and they are encouraged to bring and discuss their own data.

References: 
Fritz, C., Mehrl, M., Thurner, P. W., & Kauermann, G. (2025). Exponential random graph models for dynamic signed networks: An application to international relations. Political Analysis, 33(3). https://doi.org/10.1017/pan.2024.21

Instructors
Michał Bojanowski, Lorien Jasny

Time
Half-day, 11 August, Tuesday, 13.30-17.00

Abstract
This workshop and tutorial provide a hands-on introduction to working with temporal network data in Statnet: from exploratory data analysis and visualization to statistical modeling with Temporal Exponential-Family Random Graph Models (TERGMs). TERGMs are a broad, flexible class of models for representing the structure and dynamics observed in temporal networks. They can be used for both estimation from and simulation of dynamic network data. The topics covered in this workshop include:

-   A brief overview of exploratory data analysis with temporal network data (using the Statnet packages 'tsna' for descriptive statistics and 'ndtv' to create network movies),
-   Different types of dynamic network data (network panel data, a single cross-sectional network with link duration information, and cross-sectional, egocentrically sampled network data)
-   Statistical model elements and specification using the Statnet package `tergm`\
-   Model estimation tools for each type of data in `tergm`\
-   Model diagnostics in `tergm`, and\
-   Simulating dynamic networks from fitted models with `tergm`.

The tutorial appendices provide more details about separable models, the interpretation of the ""term operators"" in `tergm`, and a more detailed example of EDA with forward reachable sets.

Instructors
Michał Bojanowski, Lorien Jasny

Time
Half-day, 15 August, Saturday, 8.30-12.00

Abstract
This workshop provides an introduction to analyzing egocentrically sampled data with exponential-family random graph models (ERGMs) for statistical network analysis. It is 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 egocentrically sampled networks;
• defining and fitting ERGMs to egocentric data;
• interpreting model coefficients;
• checking goodness-of-fit and model adequacy; and
• simulating complete networks from the specified ERG models.

Prerequisites: Some experience R and familiarity with descriptive network concepts and statistical methods for network analysis in the R/statnet platform (particularly ERGM)."

Instructors
Örjan Bodin, Lorien Jasny

Time
Half-day, 11 August, Tuesday, 13.30-17.00

Abstract
In this workshop we will elaborate on how coupled social-ecological systems (or coupled natural and human systems) have been described and analyzed as multilevel networks and the research questions that have been addressed. Further, they will take stock in recent research that has identified different possibilities and barriers for further developments of this line of research.

Critical issues such as what are nodes and links in a social-ecological system and how to accomplish some level of comparability across different study contexts will be addressed.

They will also discuss the range of problems (design, data collection, methodological) that many have encountered when doing this kind of synthetic research.

In addition, there will be practical hands-on exercises on how conduct and understand analytical results deriving from multilevel network analyses. The analyses will be utilizing the MPNet software (http://www.melnet.org.au/pnet), which should be downloaded and installed prior to the workshop. Since MPnet require Windows, an alternative software is Statnet (https://statnet.org/), although using Statnet, not all of the multilevel analyses will be possible to conduct.

All exercises and examples will be based on real data, and both patterns of social relations among actors as well as environmental interactions among biophysical components will be examined. The workshop includes the following elements:

1. Why a social-ecological network approach? What are the presumed benefits?
2. What is a node, and what is a link in a complex social-ecological system?
3. How to move beyond just describing a social-ecological system as a multilevel network to actually ask some challenging questions, and perhaps even get some answers?
4. Investigate how patterns of social- and social-ecological relations among resource users can be related to social- and environmental outcomes.
5. Gain exposure to commonly used software for studying multilevel social-ecological networks, i.e. multilevel ERGMs implemented in MPnet.

Prerequisites:
Familiarity with the concept of networks (i.e. nodes and ties) as well as some experiences of network-centric analyses. Previous exposure to ERGM is valuable.

Instructors
Lorien Jasny, Michał Bojanowski

Time
Half-day, 11 August, Tuesday, 9.00-12.30

Abstract
This workshop provides 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, types of terms used in ERGMs, defining and fitting models to empirical data, interpreting model coefficients, goodness-of-fit and model adequacy checking, simulation of networks using fitted ERG models, and degeneracy assessment and avoidance. To attend this workshop, familiarity with basic R commands and some knowledge of SNA is assumed.

Instructor
Daniel Gotthardt, Christian Steglich, Marijtje van Duijn

Time
Half-day, 11 August, Tuesday, 9.00-12.30

Abstract
Have you ever struggled to interpret the coefficient estimates of the stochastic actor-oriented model (SAOM)? Have reviewers asked you to clarify the meaning of the numerical values of your RSiena tables? Are you worried about comparing effect sizes? Then this workshop is for you!

The SAOM is a powerful tool for modeling network evolution, but its coefficients can be challenging to interpret. Effects of network (e.g. transitivity) and covariate configurations (e.g. homophily) on tie changes are estimated on a log-odds (latent utility) scale, and their size and meaning can be influenced by rescaling and model specification. This makes direct comparisons across models or datasets problematic. This often leads to interpretational uncertainty; especially, if you are interested in the conditional probability of relationships.

In this workshop, we introduce new functions for the RSiena package that enable you to compute and interpret marginal effects, i.e., changes in the predicted probabilities corresponding to specific configurations of ego and alters, both for observation moments and simulated network trajectories (at ministeps). We will show you how to explore and visualize the non-linearities and interactions of these effects and how to generate tables of the average impact of unit changes in your predictors. By using marginal effects, you can deliver more intuitive and comparable interpretations of network processes, avoiding some pitfalls of the latent scale.

We will begin with a structured overview of existing effect size measures in RSiena and explaining the theory behind the new conditional and average marginal effects. We will then focus on how to use and report them, including plotting complex relationships between effects and tie probabilities. Practical examples and additional R scripts for computationally intensive tasks will be provided.

Target group & requirements: This is an intermediate workshop best suited for researchers familiar with R, social network analysis, and logistic regression. Prior experience with SAOM & RSiena or similar network models (e.g., ERGM, REM, or DyNAM) and software is recommended. Please bring a laptop with R installed; further instructions for setting up RSiena and the new marginal effects functions will be provided beforehand.

Instructors
Nynke Niezink

Time
Half-day, 15 August, Saturday, 8.30-12.00

Abstract
Stochastic actor-oriented models as implemented in the R package RSiena help researchers study social network dynamics and the co-evolution of social networks and social actors' individual behavior. While originally developed for directed networks and discrete behavior, the model now accommodates a wide range of data types as dependent variables, including undirected networks, two-mode networks, multiplex networks, and continuous behavior. Over the years, a large selection of effects has been implemented for stochastic actor-oriented models. Many of these were motivated by the diverse set of research questions network researchers have about social dynamics. Yet, you may still run into the problem of wanting to study a social mechanism for which the RSiena manual does not contain a matching effect. In this case, if you feel comfortable programming in R, you may want to implement an effect in RSiena yourself.

This workshop will discuss how to create an effect in RSiena. We will go through all the steps of developing an RSiena effect, starting from how to translate a social mechanism into an effect function. Even if you do not plan to implement effects yourself, this is a very useful skill to have, as it will allow you to think of creative ways of using existing effects. We then discuss implementation and testing. Since the back-end of the RSiena code was implemented in C++ for computational efficiency, creating RSiena effects involves coding in both R and C++. The workshop will give a brief introduction to C++, discussing just those parts you need to be able to create your effect. Finally, we will discuss how you can decide, per effect, depending on your coding experience, whether to implement it yourself or to ask for help.

The target audience for this workshop consists of RSiena users who feel comfortable programming in R (e.g., writing a function, writing for-loops, etc). No prior experience in C++ is required. The workshop will not introduce the stochastic actor-oriented modeling framework and will only focus on implementing effects. Please refer to the EUSN 2026 workshop list for introductory workshops on RSiena.

Instructor
Nynke Niezink

Time
Half-day, 15 August, Saturday, 14.30-18.00

Abstract
Social actors are often embedded in multiple social networks, and there is a growing interest in studying social systems from a multiplex network perspective. This workshop offers a practical introduction to the multip2 R package for analyzing multiplex network data. This package implements the multiplex mixed-effects network model in the p2 (van Duijn et al., 2004) modeling framework. The multiplex p2 model allows researchers to represent cross-layer dyadic dependencies as fixed effects and actor-specific dependencies as random effects, while also considering the influence of covariates in the analysis of cross-sectional, directed binary multiplex network data. In the workshop, we will traverse the entire path from multiplex network research questions to descriptive statistics, modeling, goodness of fit, and conclusions. We will address what questions can be answered by a multiplex p2 analysis, and which cannot. We estimate the model in a Bayesian framework, and will introduce Bayesian estimation at an introductory level.

Workshop topics include:
– Multiplex network research questions
– Introduction to the multiplex p2 modeling framework
– A brief introduction to Bayesian analysis
– Overview of the R package multip2 and the underlying estimation procedure in stan
– Model fitting and convergence diagnostics
– Interpretation of model coefficients
– Goodness-of-fit assessment via simulations and plotting

Note: participants are expected to have a basic familiarity with R for the practical segment of the workshop and some understanding of statistical inference for the conceptual portion.

Instructor
Srebrenka Letina, Adrian Lüders, Dino Carpentras, Philip Warncke

Time
Full day, 11 August, Tuesday

Abstract
The network approach is increasingly employed to explore relationships among concepts, such as co-endorsement patterns among socio-political attitudes, co-occurring health conditions, or the relationships between psychological variables. Given the multitude of approaches available for constructing and analysing such networks as well as their application across different fields of study, determining the most appropriate methods and analyses can be challenging.

In this workshop, we aim to provide:

Theoretical Framework: An overview of the theoretical basis for applying network analysis to study relationships among individual-level variables.

Methodological Approaches: An exploration of existing methodologies for constructing networks and robustness testing of their estimations. In the second part of the workshop introduces Response-Item Networks, a novel member of belief-network model family which maps co-endorsement patterns among socio-political attitude data onto an interpretable latent space (e.g., the political left-right spectrum). Following the conceptual introduction to the method, we will conduct a guided tutorial to the ResIN package for R.

Analytical Techniques: A comprehensive set of analyses applicable to co-occurrence or correlation networks on cross-sectional data, including basic descriptive analysis, filtering methods, community detection, centrality analysis, network comparisons, and more.

We will offer a critical assessment of methods tailored to specific types of data and interpretations. Practical demonstrations of the methods and analyses will be conducted using several datasets and different R packages with an emphasis on new ResIN package, and we will share our R code. In the final segment of the workshop, participants are encouraged to discuss the application of these methods to their specific datasets.

Instructor
Elisa Belotti

Time
Full day, 11 August, Tuesday

Abstract
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.

Instructor
Vladimir Batagelj

Time
Full day, 15 August, Saturday

Abstract
A two-mode network N = ((U,V),L,w) is based on two disjoint sets of nodes U and V. Each link (u,v) from L has one end-node in the set U and the other end-node in the set V. The function w assigns to each link its weight. The network N can be represented with a rectangular matrix W = [w(u,v)].

Two-mode networks are often encountered in the analysis of real-life systems. Some examples: ((Persons, Societies), is member of, # of years), ((Customers, Goods), bought, value), ((Recipes, Ingredients), contains, quantity), ((Papers, Keywords), is described by, 1), ((Country, Song), assigned, points), etc.

The standard approach in the analysis of binary (w = 1) two-mode networks is the use of projections row(W) = W ∗ WT or col(W) = WT ∗ W that transforms the network into a one-mode network. A detailed analysis reveals that in the projection, some nodes from the other set are overrepresented [2, 4]. To obtain fairer projections, the fractional approach is used – the projection is applied to the normalized network. We also extend projections to temporal twomode networks [3]. Instead of by projections, we can transform a given two-mode network also into a one-mode (dis)similarity (Salton, Jaccard, Euclid, etc.) network/matrix on the selected mode.

Network multiplication can also be used to create derived networks. For example from bibliographic networks WA (authorship, works × authors) and WK (works × keywords) we obtain the network AK = WAT ∗ WK linking authors to keywords (used in their works); and for citation (one-mode) network Ci the derived network ACi = WAT ∗Ci∗WA is a network of citations between authors. Again, the question of the fairness of the obtained weights appears[2].

To identify important subnetworks in the obtained weighted networks, procedures such as cuts, clustering, cores, or island procedures are typically used [1].

At the workshop, we will present a theoretical background of the proposed approaches, the corresponding algorithms implemented in the igraph-based R-package netsWeight (and Python packages Nets and TQ [5]), and illustrate them on selected (large) real-life networks. The workshop materials will be available at [6].

Instructor
Matteo Magnani

Time
Half-day, 11 August, Tuesday, 13.30-17.00

Abstract
Many real social networks contain multiple types of ties, for example representing different types of social interactions or different contexts where interactions happen. This workshop presents the main concepts and methods for explorative multiplex network analysis, with a focus on how to apply them in practice. In the workshop, multiplex networks will be modelled as multilayer networks, where each type of edges constitutes one of multiple layers. Covered topics include: visualisation, micro-level analysis (actor centralities, distances), meso-level analysis (communities), and macro-level analysis (comparison of different edge types). For the practical part we will use the CRAN package multinet (for which a Python version is also available).

By the end of the workshop, participants will be able to:
- model social networks as multilayer networks;
- include exploratory multilayer network analysis methods into their research designs;
- apply multiplex network analysis to their research using computer code.

Instructors
Alvaro Uzaheta, Maria Eugenia Gil-Pallares, Marion Hoffman, James Hollway, Christoph Stadtfeld

Time
Full day, 11 August, Tuesday

Abstract
This full-day workshop provides comprehensive training in relational event modeling using the goldfish R package. The study of relational events is growing in social network research, driven by the increasing availability of data. For example, data collected from digital traces of individuals’ interactions —such as communication exchanges, transactions, and collaboration— provide in-depth details regarding the timing or sequence of relational actions between actors. The workshop is structured in two parts: a foundational morning session introducing core concepts and methods, followed by an afternoon session covering advanced topics tailored to participant interests.

Morning Session (3 hours): Foundations of Relational Event Modeling

The first half provides an introductory theoretical overview from a social science perspective, complemented by a hands-on tutorial on the different models implemented in the package. 

Dynamic Network Actor Models (DyNAM) for investigating relational events as actor-oriented decision processes, including:
Rate: Actors compete to create the next relational event (Hollway, 2020).
Choice: The active actor chooses the event's receiver from the same set of nodes (Stadtfeld and Block, 2017) or from a different set of nodes (Haunss and Hollway, 2023).
Choice coordination: The creation of coordination ties as a two-sided process (Stadtfeld et al., 2017), as in studies analyzing agreements between countries.
Relational Event Models (REM) investigating relational event models as a tie-oriented process (Butts, 2008) and accounting for right-censoring (Stadtfeld and Block, 2017). 
Core topics include:
Model specification, estimation, and interpretation in R
Practical data preparation and preprocessing for relational event analysis

Afternoon Session (3 hours): Advanced Applications

The second half addresses advanced modeling challenges based on participant demand and instructor availability. Potential topics include:
Random Effects for Actor Heterogeneity: Implementing latent variable models to account for unobserved heterogeneity across actors or multiple event sequences (Uzaheta et al., 2023).
Face-to-Face Interaction Dynamics: Applying DyNAM-i models to analyze conversational group formation and interpersonal interaction patterns, particularly relevant for RFID sensor data and similar observational settings (Hoffman et al., 2020)
Multiple Time Scales: Modeling relational processes operating simultaneously at different temporal resolutions, addressing complex dependencies across time scales.
Participants will engage with real-world examples and hands-on exercises throughout both sessions, enabling immediate application of methods to their own research contexts.
Prerequisites: Participants should have working knowledge of R and model-based statistical inference (e.g., logistic regression). Familiarity with social network concepts is beneficial but not required.

Required Software: Participants must bring a laptop with the following installed:

• R statistical computing environment
• goldfish package with dependencies 

References:
·  Butts, C. (2008). ""A Relational Event Framework for Social Action."" Sociological Methodology 38(1): 155–200. 
·  Haunss, S., and Hollway, J. (2023). ""Multimodal Mechanisms of Political Discourse Dynamics and the Case of Germany's Nuclear Energy Phase-Out."" Network Science 11(2): 205–23. https://doi.org/10.1017/nws.2022.31
·  Hoffman, M., Block, P., Elmer, T., and Stadtfeld, C. (2020). ""A Model for the Dynamics of Face-to-Face Interactions in Social Groups."" Network Science 8(S1): S4–25. https://doi.org/10.1017/nws.2020.3
·  Hollway, J. (2020). ""Network Embeddedness and the Rate of Water Cooperation and Conflict."" In Networks in Water Governance, edited by Manuel Fischer and Karin Ingold, 87–113. Cham: Palgrave Macmillan. https://doi.org/10.1007/978-3-030-46769-2_4
·  Stadtfeld, C., and Block, P. (2017). ""Interactions, Actors, and Time: Dynamic Network Actor Models for Relational Events."" Sociological Science 4(14): 318–52. https://doi.org/10.15195/v4.a14
·  Stadtfeld, C., Hollway, J., and Block, P. (2017). ""Dynamic Network Actor Models: Investigating Coordination Ties Through Time."" Sociological Methodology 47(1): 1–40. https://doi.org/10.1177/0081175017709295
·  Uzaheta, A., Amati, V. and Stadtfeld, C. (2023). ""Random Effects in Dynamic Network Actor Models."" Network Science 11(2): 249–266. https://doi.org/10.1017/nws.2022.37

Instructor
Bernie Hogan

Time
Full day, 15 August, Saturday

Abstract
Since its first release almost ten years ago, Network Canvas has been growing in usage and features. It notably won the Richards award for best software at Sunbelt 2025 in Paris. Today it represents a comprehensive solution to social network data collection as well as a free / open-source platform for such work.

In this workshop members of the core Network Canvas team will introduce Network Canvas including best practices for collecting sociocentric and self-reported data, including:

• How to collect self-reported names,
• Considerations for edge-data collection: when to consider drawing versus surveying,
• How to consider branching surveys / skip flow,
• How to align your mental model with the data structure you will analyse, and
• Novel and new features such as privacy-sensitive encrypted name collection, geographic labelling with maps, and family tree data.

An exciting feature of Network Canvas for EUSN 2025 is that the entire software is now available in-browser, including the survey builder (“Architect”) and the deployment software (“Fresco”). This means neither the researcher nor the interviewee need to download or install applications. We will cover this transition, show how to work with our hosted Network Canvas install as well as point to resources for your own installation if preferred.

There are no formal prerequisites for this session. During the session you will be able to enter short URLs given by the instructor to specific example protocols. The instructor may refer to “Interviewer” and “Architect”, both of which can be downloaded from www.networkcanvas.com but plan to have all work done in browser. The instructor will show downloaded data in Microsoft Excel and run an example analysis script from the Network Canvas documentation page within RStudio or a visualisation within Gephi. Little time will be spent on analysis with most time on instrument design. However, attendees may wish to have MS Excel (https://www.office.com/ ), RStudio (https://posit.co/download/rstudio-desktop/ ), or Gephi (https://gephi.org) installed to mirror those brief activities.

Instructor
Tom Snijders

Time
Full day, 15 August, Saturday

Abstract
This workshop is intended for participants who have experience in working with RSiena.

Topics treated will be the following – all in the framework of modelling network panel data using the RSiena package

1. Transition to the new function names.
2. Parameter interpretation: semi-standardized parameters; entropy-based approach to explained variation.
3. Score-type tests.
4. Problems with convergence: various kinds. 
5. Elementary effects and contextual effects. 
6. Multivariate networks: cross-network effects; with attention to the associated hierarchy requirements.
7. Two-mode networks, and their co-evolution with one-mode networks.
8. Some effects that are little known, but which may be useful for analyzing two-mode networks.
9. If time allows: Non-directed networks.
10. If time allows: Valued networks (two kinds: networks with weak and strong ties; signed networks).

SIENA website: http://www.stats.ox.ac.uk/~snijders/siena