This 3-hour workshop provides an introduction to the R programming language for those without any previous or limited experience. It will introduce the tidyverse – a set of functions and packages for data processing, cleaning, and visualisation in R. In particular, we will focus on dplyr for data processing, ggplot2 for visualisation, and Rmarkdown for creating reports. We will go on to demonstrate how the tidyverse can be applied to social network analysis - more specifically through the use of the tidygraph package. The tidygraph permits you to utilise the underlying grammar structure of the tidyverse when dealing with graph objects in R. By using the tidygraph package you can manage edgelists and network attributes in a single object, along with implementing analysis on these objects. The tidyverse allows you to create tidy data frames, whilst the tidygraph allows you to create tidy graph objects – or tidy networks! Learning Outcomes: By the end of the session participants should be able to: • Use R and RStudio. • Make use of the tidyverse for data processing – more specifically preparing datasets for SNA. • Visualising networks in R using ggplot2 (part of the tidyverse) and tidygraph. • Create tidygraph objects and undertake some initial network analysis using the tidygraph package. These users will benefit from gaining an insight into how to use R for data processing and social network analysis following the tidy philosophy.

Many real social networks contain multiple types of ties, for example representing different types of interactions or different contexts where interactions happen. Through the use of R libraries for the analysis of multiplex networks, this workshop explores the main theoretical concepts and analysis methods in a practical way. Participants will be introduced to the key principles of multilayer social network analysis, including intra-layer and inter-layer interactions, measures and community detection methods. Emphasis will be placed on understanding how multilayer structures differ from traditional single-layer networks and the unique insights they provide. Multiplex networks have been studied in different disciplines, including sociology, computer science, and physics, because of their ability to provide richer, more qualitative information than simple graphs, but still allow quantitative processing. The main topics covered are: visualization, micro-level analysis (actor centrality and role of edge types), meso-level analysis (communities), macro-level analysis (comparison of different edge-types), and integration. The practical component will focus on using two main R packages multinet and INetTool to analyze multilayer networks. With multinet participants will learn how to model multilayer networks, perform descriptive analyses, and compute multilayer metrics, while with INetTool participants will explore how to integrate networks, including merging heterogeneous datasets, and extracting insights from real-world case studies. Case studies will be drawn from diverse domains, such as social media interactions, organizational collaboration, and others, providing participants with practical examples of how to apply these tools to real-world problems. By the end of the workshop, participants will: - Understand the theory of multilayer social networks. - Gain proficiency in using multinet and INetTool for network analysis. - Be equipped to apply multilayer network analysis to their research.

Schedule: 3 hours Limited to 30 seats 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.

This workshop provides an advanced introduction to `goldfish.latent`, an R package that extends relational event modeling by incorporating latent variable models. Participants will learn to model actor heterogeneity through the package's implementation of random effects powered by Stan. Practical examples and hands-on exercises will guide attendees through model specification, estimation, and interpretation, enabling them to apply these advanced methods to their relational event data. A particular focus will be given to analyzing multiple sequences as a case study for using random effects, highlighting the package's flexibility in handling complex relational event structures. Prerequisites: Participants should be familiar with R and the `goldfish` package. Those new to goldfish are encouraged to attend the introductory “Modeling Relational Events in R Using goldfish” workshop. What to Bring: • A laptop with the following installed: o R statistical computing system o Stan (via `cmdstanr` or `rstan`) o `goldfish` and `goldfish.latent` packages with all dependencies • Installation links: o goldfish.latent: https://github.com/snlab-ch/goldfish.latent o Stan: https://mc-stan.org/cmdstanr/ References: • Stadtfeld, Christoph, and Per Block. 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. • Uzaheta, Alvaro, Viviana Amati, and Christoph Stadtfeld. 2023. "Random Effects in Dynamic Network Actor Models." Network Science 11(2): 249-266. https://doi.org/10.1017/nws.2022.37. Length: 3 hours Participants: 30