This workshop introduces the major streams of social network theory, taking a conceptual view grounded in and contrasted with the broader landscape of social theory. This is not a course on network methodology, although mathematical concepts such as centrality and structural equivalence are discussed. The workshop is intended as a survey of the major conversations network researchers from the social sciences are engaged in. It is suggested for those new to the network perspective who are interested in a map of the theoretical landscape. The workshop does not use any software or data. Network Theory -- Outcomes of network variables/mechanism Here we consider network theorizing in both the social capital literature (e.g., weak ties, structural holes, social resource theory) and the contagion literature (e.g., interpersonal influence, diffusion of innovation). We examine how concepts like centrality and core-periphery structures are interpreted in these different contexts. Topics include multiple levels of analysis, theoretical network mechanisms, and social cognition (perceived ties). Themes include the interplay of node characteristics and network structure, as well as … Theory of Networks -- Antecedents of Network Variables This section deals with theories of tie formation, why networks have the shapes they do, and why actors occupy the network positions they do. Topics include homophily, preferential attachment, mechanisms of choice and opportunity, balance theory, etc.

This workshop will provide a comprehensive introduction to the problem of Community Detection (CD), i.e. dividing a network into groups of nodes, along with materials and code for its implementation. It will cover the main families of algorithms and the major challenges in implementation, as well as means to compare and evaluate solutions. The workshop will mostly focus on the standard problem of non-overlapping partitions on undirected, unweighted networks, although other problem and network variations will be briefly reviewed at the end. While the code and examples will be provided in python within an interactive online platform, so familiarity with the language will not be required and there is no setup needed previous to the workshop. Overview of content: - Introduction to the problem and applications - Overview of the main families of problem definitions and the most common algorithms, and their implementations - Evaluation of CD solutions - Comparing and combining multiple solutions - Quick overview of variations: overlapping CD, multiplex, temporal

This workshop introduces participants to constructing and analyzing term co-occurrence networks using the open-source Python package knowknow. Co-occurrence networks are particularly effective for analyzing small, linguistically diverse datasets, enabling researchers to identify features and trends that may appear in only a few documents. Participants will gain hands-on experience analyzing a curated dataset of journal articles from anthropology, economics, political science, psychology, and sociology (1970–2020), with the option to bring and work with their own datasets. The session covers techniques for building co-occurrence datasets from academic texts and preparing them for analysis; methods for identifying meaningful terms, their relationships, and the structural properties of co-occurrence networks, such as clusters, hubs, bridges, and temporal patterns; and practical guidance on sharing workflows and datasets using Harvard Dataverse and GitHub to ensure replicability and collaboration. Morning Session: Building and Visualizing Semantic Co-occurrence Networks. 1) Overview of co-occurrence networks and their application to social science research. 2) Preparing a dataset and building initial co-occurrence networks using knowknow. 3) Visualizing networks using built-in tools. 4) Exporting datasets, documenting workflows, and publishing on open platforms. Afternoon Session: Interpreting Semantic Co-occurrence Networks. 1) Temporal trends and structural features of semantic networks. 2) Formulating and answering research questions about the dynamics of the social sciences. 3) Hands-on projects. Prerequisites: Beginner-level familiarity with Python (basic scripting, running code in Jupyter Notebooks). No prior experience with knowknow is required. Participants should bring a laptop with Python pre-installed or access to an online Python environment (e.g., Google Colab).

This three-hour workshop provides an in-depth introduction to BlockmodelingGUI, a cutting-edge R package designed to simplify and enhance blockmodelling techniques in network analysis. Blockmodelling is a powerful method for identifying and interpreting patterns in relational data, making it invaluable in fields such as sociology, political science, and organisational studies. By integrating an intuitive graphical interface with the robust analytical capabilities of R, BlockmodelingGUI empowers researchers to uncover structural insights without requiring extensive coding expertise. Participants will engage in a combination of conceptual discussions and hands-on exercises, exploring the theoretical underpinnings of blockmodelling and applying these methods to real-world datasets. The workshop will cover essential workflows, from data preparation and model configuration to result interpretation and visualisation. Attendees will also discover advanced features of the package, including optimisation techniques and customisation options, enabling them to tailor analyses to their specific research questions. This session is designed for researchers, data scientists, and professionals eager to enhance their understanding of network structures. By the end of the workshop, participants will have the practical skills to harness BlockmodelingGUI in their own projects and a deeper appreciation of how blockmodelling can illuminate hidden dynamics within complex systems. Whether you are a seasoned network analyst or a newcomer to the field, this workshop offers valuable insights and tools to elevate your analytical capabilities. ## Detailed workplan 1. Introduction to Blockmodelling * Key concepts and applications in social network analysis. * Advantages of blockmodelling for understanding relational structures. 2. Getting Started with BlockmodelingGUI * Installing and setting up the package. * Overview of the graphical user interface. 3. Hands-On Analysis * Importing and preparing network data. * Building and customising blockmodels. * Interpreting results and generating visualisations. 4. Advanced Features * Optimisation techniques and parameter tuning. * Exporting and integrating results with other analyses. 5. Case Studies * Real-world examples demonstrating the package’s capabilities. * Collaborative exercises to reinforce learning. ## Format * Duration: [Specify, e.g., Half-day or Full-day workshop] * Structure: Introduction (20%) and hands-on practice (80%). * Materials: Participants will receive datasets/pre-configured R environments. ## Requirements * Participants should bring laptops with R and RStudio pre-installed. * The workshop organisers will provide a detailed setup guide before the even

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.

In this 3-hour workshop, you will get hands-on experience with SNA Toolbox – web-based software for the collection, visualisation, analysis and rapid reporting of social network data. SNA Toolbox is a comprehensive all-in-one network package that allows you to collect network data and have it instantly available in network-ready form for analysis (e.g., standard network metrics, indegree, clustering, etc…), with the ability to create a pipeline for online reports for research clients/partners/customers/participant groups that are securely accessible online. Points covered include: - System overview, including features, security, and user roles - Using the survey designer to construct social network and standard attribute questions, as well as automated calculations of scale items. - Network analysis packages that immediately utilise the data collected without the need for data transformation. - Network visualisation tools, algorithms, nodal attributes, and selected node information - General data visualisation tools (e.g., charts, graphics, etc….) - Reporting: templates, pre-configurations, insights tools, and rapid reporting to clients on researcher-selected metrics. No software is required. You will be supplied with login credentials for the web-based application for the workshop and beyond.

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.

This 3-hour workshop provides an introduction to statistical methods for analyzing social networks. The focus is on nodal and dyadic level analysis. We will be using R packages migraph, sna, and xUCINET to perform these analyses. The course outline is as follows: 1) testing a network’s basic properties using conditional uniform graph (CUG) test (e.g., reciprocity, homophily) 2) nodal level statistical tests 3) permutation-based comparisons between groups of nodes 4) QAP correlation and linear regression – the underlying logic of QAP, data format etc. 5) QAP GLM – logistic, poisson, cognitive-social-structures, and other types and extensions

The network approach is increasingly employed to explore relationships among concepts, specifically the relationships between co-occurring health conditions (e.g., binary health condition indicators from hospital episode data) and the relationships between psychological variables (continuous scales from survey data). Given the multitude of approaches available for constructing and analyzing such networks and their application across different fields of study, determining the most appropriate methods and analyses can be challenging. In this workshop, we will cover: Theoretical Frameworks: An overview of the theoretical basis for applying network analysis to study relationships among health conditions or individual attributes. Methodological Approaches: An exploration of existing methodologies for constructing networks and robustness testing of their estimations. Analytical Techniques: A comprehensive set of analyses applicable to co-occurrence or correlation networks, including basic descriptive analysis, filtering methods, community detection, centrality analysis, and network comparisons. We will offer a critical assessment of methods tailored to specific types of data and interpretations. Practical demonstrations will cover a range of methodological options and the various R packages to conduct them. In the final segment of the workshop, participants are encouraged to discuss the application of these methods to their specific datasets. Names and contact information of organizers: Srebrenka Letina; Srebrenka.letina@glasgow.ac.uk Mark McCann; Mark.Mccann@glasgow.ac.uk Length of the workshop: 3 hours Maximum number of attendees: 30

In the field of scientometrics, methods derived from Social Network analysis (SNA) and Natural language processing (NLP) are among common techniques used to analyze and visualize graphs. These methods focus on both the structural and morphological aspects of the social networks investigated, whether or not their actors are localized. SNA and NLP approaches are not specifically interested in the spatial component (i.e. localization, interactions, geovisualization) of social networks. Their complementarity with gravitational approaches, combining analysis of actors' positions and separations (distance, proximity, neighborhood) has nevertheless been widely used in the field of spatial analysis in geography. This workshop aims to present a scientometric co-authorship' analysis on a preselected topic (e.g., low carbon initiatives, climate change, AI in transportation), using Cortext Manager [1] and Arabesque [2]: two web applications respectively mobilized to geocode authors' affiliations addresses at several geographical scales and filtering and exploration spatial networks for thematic mapping purposes. Emphasis will be placed on examining the contributions of different countries or groups of countries to scientific advancements in the selected field and the collaboration patterns that emerge. This hands-on session will guide participants through spatial data analysis and network analysis enabling them to identify thematic and territorial patterns within scientific communities. For doing that, participants will learn how to classify documents by lexical extraction and semantic clustering, or by tagging the textual content of scientific articles with the corresponding Sustainable Development Goals (SDG) categories. This will be followed by the geocoding of authors' affiliation addresses to pinpoint their exact geographic locations. Next, geographic/spatial aggregation methods will be explored, preparing data at different scales, from the address scale to larger meso-level units of analysis, such as: Eurostat’s NUTS3, OECD’s Functional Urban Areas or NETSCITY’s perimeters (most active urban areas in science production [3]). Finally, the resulting co-author’ spatial networks files will be geovisualize in Arabesque, a cartographic tool based on the paradigm of visualization cartography. Several methods of statistical filtering with options adding contextual geographic information or cartographic (re)projections will be applied to the dataset. A particular attention will be paid to the cartographic design of actors' interrelations at different scales through arrows: to their geometry and their semantics, playing on their graphic semiology. Participants will have time to play with the datasets and tools covered in the workshop, with guidance from the trainers and access to a set of materials: a mini-website giving access to all the resources, including datasets, tutorials and examples of results. [1] https://docs.cortext.net/space/ [2] https://arabesque.univ-eiffel.fr/ [3] https://www.irit.fr/netscity/prod/public/intro/ This workshop is part of RETICULAR (RÉseaux, Territoires en Interactions et interrelations Cartographiques), a collaborative research program funded by Université Gustave Eiffel and supported by the LISIS, Aménagement Mobilités Environnement (AME-splott) and Composants et systèmes(COSYS-grettia) départements, with the collaboration of CNRS (Géographie-cités).

Prerequisites: Some experience with R and familiarity with descriptive network concepts and statistical methods for network analysis in the R/statnet platform is expected. This workshop also assumes familiarity with ERGMs. Synopsis: This workshop will provide an introduction to modeling of network dynamics in continuous time using ERGM generating processes (EGPs). The exponential family random graph models (ERGMs) are a widely used framework for describing graph distributions, allowing flexible and parsimonious specification of both inhomogeneity (i.e., some ties are more likely than others) and dependence (i.e., some ties depend on others). EGPs complement ERGMs by providing ways of specifying continuous time dynamics whose long-run behavior recapitulates a specified ERGM distribution - thus allowing for dynamic network models that are consistent with specific cross-sectional behavior. In this session, we will begin with an overview of known classes of EGPs, with an eye to understanding the types of dynamic behavior embodied by each (and where they might be appropriate as empirical models). We will then discuss simulation and calibration of EGPs within the R/statnet platform, using the ergmgp package. We will show examples of the use of EGPs to generate dynamics consistent with cross-sectional network data combined with information on tie durations, including continuous time generalizations of the separable temporal ERGMs (STERGMs). Attendees are expected to have had some prior exposure to R and statnet, and completion of the statnet ERGM workshop session is strongly suggested as preparation for this session (as we will make extensive use of the ergm package). statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac). statnet packages can be used to handle a wide range of simulation and analysis tasks, including support for large networks, statistical network models, network dynamics, and missing data.

Those wishing to use the R programming language for network analysis now have a plethora of choices when it comes to libraries. In this workshop, we survey the main packages used for network data management, analysis, and visualization. We will cover 1) importing network data (from actual files), 2) network objects and attributes, 3) computing basic descriptives (attribute distribution, mixing matrix, density, degrees, betweenness, closeness), and 4) visualization (layouts, node aesthetics). These will be done side by side for the different packages, as well as discussion of the strengths and weaknesses of each. We conclude with time for attendees to work either on toy datasets or with their own data with help from instructors. This workshop is a unification of workshops "Using R and 'igraph' for Social Network Analysis" and "Introduction to Social Network Analysis with R and statnet" that has been offered on Sunbelt and EUSN conferences since 2011. It will serve as an introduction for those wishing to take "An introduction to ERGM with Statnet", or other Statnet-related workshops on the program.

This workshop offers an introduction to the R programming language and its tools to represent, manipulate, and analyze egocentric or personal network data. Topics include: introduction to ego-network research and data; data structures and network objects in R; visualizing ego-networks; calculating measures on ego-network composition and structure; converting ego-network measures to R functions; applying these functions to many ego-networks. The workshop heavily relies on R tidyverse packages for data science, showing how they can be used to easily conduct common operations in ego-network analysis and scale those operations up to large collections of networks. We'll cover specific packages for network analysis (igraph, network, egor), data management (dplyr) and programming (purrr). No previous familiarity with R is required; participants only need a laptop with R and RStudio installed. This workshop has been taught for the past several years at different international conferences, including INSNA's Sunbelt and EUSN meetings. It draws on concepts and methods presented in "Conducting personal network research: A practical guide" by Christopher McCarty, Miranda Lubbers, Raffaele Vacca and José Luis Molina (Guilford Press). More details on the workshop's materials and requirements are here: raffaelevacca.com/egonet-r.

This 6-hour workshop provides an overview of network measures, as well as a short introduction into data collection and data management. The focus is on complete networks, although some topics might also be useful for analyzing egonetwork data. The course outline is as follows: - Introduction to social networks, different types of networks (including two-mode/affiliation networks and valued networks) - Different types of datastorage: adjacency matrices, nodelists and edgelists, and incorporating attributes - Basic visualization - Centrality measures - Whole network structural measures (density, centralization) - Subgroups, such as cliques, as well as community detection

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 networks 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. They 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.

Theory matters! It guides our attention in research, it gives us expectations for empirical analysis, and it allows us to interpret results as examples of wider significance. Traditionally, network research focuses more on methods than on theory, leading to laments about the lack of theory. Over the last 35 years, there have been important advances in this regard. Now we have a variety of theoretical approaches to networks particularly from sociology (rational choice, analytical sociology, relational sociology etc.) available, as well as a number of middle-range theoretical concepts (social capital, network mechanisms). However, often enough, researchers do not know which concepts and approaches work well with their research. The workshop gives an introduction and reflection into the general perspective of social network analysis, it offers an overview of the currently most important concepts and theoretical approaches to social networks, and provides for a forum for participants to discuss their own empirical research in relation to theory. The focus of the workshop lies on theories that give answers to the questions: What are social networks? Why, and how, do they matter for social phenomena? The following topics will be covered: ‒ the general perspective of network research in the social sciences, with its difference to other approaches; ‒ what is theory, and how does it matter in the research process? ‒ networks as social capital; ‒ two-mode networks; ‒ varieties of relational sociology (inspired by pragmatism, symbolic interactionism, and by Harrison White); ‒ network mechanisms (foci-of-activity, homophily, institutionalized role patterns, reciprocity, transitivity, preferential attachment, social control, brokerage, access to information), the epistemological status of network mechanisms; ‒ methodology: which theoretical approach work with which methods? ‒ what concepts and theoretical approaches fit the attendants’ empirical research projects? Much of the workshop will be run as presentations by the lecturer, complemented by short discussions among the participants. I will also be available for one-to-one counselling. A selection of texts will be sent to the participants, in case they want to prepare for the workshop. However, reading these is not mandatory.

In today’s complex and interconnected workplaces, understanding the dynamics of teams goes beyond traditional organizational charts. Teams are influenced not only by formal hierarchies but also by the informal networks that drive collaboration, communication, and innovation. As the world grapples with unprecedented challenges—ranging from climate change, global pandemics, and geopolitical instability to technological disruption and social inequality—effective problem-solving and collaboration have become more critical than ever. Addressing these multifaceted issues requires interdisciplinary approaches and high-performing teams that can adapt quickly, communicate effectively, and innovate relentlessly. This workshop introduces participants to the concept of the Archintor®, a transformative framework leveraging social network analysis to identify, analyze, and optimize team dynamics and structures to meet these demands. The Archintor® concept (architect + instructor + facilitator) was first introduced in 2023 via a PLOS ONE publication. It represents a paradigm shift in applied social network analysis for team building, emphasizing how expectations shape network structures. By identifying the “ideal” network structure perhaps even before a team has formed, an Archintor® is tasked with designing and fostering expectations that drive specific interactions, ultimately reshaping team networks to enhance effectiveness and success. This workshop grounds participants in existing research and practice, drawing on findings from two recent studies to illustrate the power of social network analysis in real-world applications. Participants will explore: ● The foundations of social network analysis and its role in identifying team types and guiding team formation and development. ● How to identify and leverage critical network types and roles such as connectors, boundary spanners, and structural holes. ● Strategies for shifting network structures to achieve desired configurations. ● The importance of communication, learning, and social connections in fostering ideal team structures. ● Best practices for designing and maintaining effective team networks. ● Tips for conducting longitudinal network analysis to monitor and refine team dynamics. The workshop features several interactive experiences, including small group problem-solving activities and collaborative discussions. Participants will gain hands-on practice in mapping and analyzing networks, as well as developing strategies to intentionally shift team dynamics to align with organizational goals. By the end of this session, participants will be equipped with practical tools and actionable insights to become an effective Archintor®, capable of designing and guiding network structures that maximize team potential. This workshop is ideal for network analysts, evaluators, organizational development specialists, and researchers interested in leveraging social network analysis to build and sustain high-performing teams that can address today’s unprecedented global challenges. 3 Hours, 30 Maximum Participants

Prerequisites: Some experience with R and familiarity with descriptive network concepts and statistical methods for network analysis in the R/statnet platform is expected. Synopsis: This workshop session will provide an introduction to the analysis and simulation of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the 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, assessment, and simulation of dyadic relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Attendees are expected to have had some prior exposure to R and statnet, and 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 parametric statistical methods is strongly recommended, and some knowledge of hazard or survival analysis will be helpful. statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac). statnet packages can be used to handle a wide range of simulation and analysis tasks, including support for large networks, statistical network models, network dynamics, and missing data.

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. 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. We will go through the several phases of developing an RSiena effect, going from social mechanism to effect definition to implementation and testing. We will also see how implementing effects can go wrong and discuss how you can debug your work. 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 but only focus on implementing effects. Please refer to the Sunbelt 2025 workshop list for introductory workshops on RSiena. Length: 3 hours Capacity: 30 people

The ACT (Activate, Connect, Transform) model aims to guide the design, implementation, and evaluation of social and collaborative interventions that Activate, Connect, and Transform individuals, organizations, health systems, and communities. The ACT model responds to pressing needs in healthcare and community action: how to meaningfully engage patients in decision-making, research, and policy; how to leverage social networks for the dissemination and implementation of high-quality innovations; and how to create networks of learning and improvement in healthcare and community settings. This is essential in our rapidly changing landscapes, where bridging formal and informal social networks and relations can enhance outcomes and quality of services and equip health and social systems to respond dynamically to emerging needs and crises. The three pillars of ACT involve: Activate: empowering individuals, organizations, and communities with the motivation, skills, and strategies to mobilize resources and foster relationships. Connect: Building and nurturing supportive relationships, networks of influence and knowledge sharing, and partnerships among individuals, teams, and communities to strengthen collective capacity and achieve shared goals. Transform: Driving improvement in behaviors, processes, and outcomes by implementing and sustaining evidence-based innovations. The workshop Agenda: - Introduction to the three-pillar approach - Activate interventions: •“Network diagnostics”/charting at the individual or community levels to provide network actors with a bird’s eye view of their existing networks and potentials for further activation.(Yousefi Nooraie, et al., 2021) •Asset mapping - Connect interventions: •Strategies to facilitate connectivity and optimize social structure, following the framework developed by Yousefi Nooraie, et al. (2021) - Transform interventions: •Strategies to enhance the dissemination and implementation of valued interventions using networking strategies, following the framework developed by Bunger and Yousefi Nooraie, et al. (2023) - Cyclic approach to intervention refinement - A quick introduction to evaluation •Approaches to assess network evolution, social activation, and resilience building, with an emphasis on mixed-methods analysis (Yousefi Nooraie et al., 2020) With this unique three-pillar approach—Activate, Connect, and Transform—the ACT model aims to inform the design of interventions to build dynamic, resilient, and inclusive networks where individuals are engaged, networks are optimized for knowledge sharing and support, and dynamically respond to emergent needs. Workshop length: 3 hours Maximum number of attendees: 20 References: Bunger, A. C., Yousefi-Nooraie, R., Warren, K., Cao, Q., Dadgostar, P., & Bustos, T. E. (2023). Developing a typology of network alteration strategies for implementation: a scoping review and iterative synthesis. Implementation Science, 18(1), 10. Yousefi Nooraie, R., Mohile, S. G., Yilmaz, S., Bauer, J., & Epstein, R. M. (2021). Social networks of older patients with advanced cancer: Potential contributions of an integrated mixed methods network analysis. Journal of geriatric oncology, 12(5), 855-859. Yousefi Nooraie, R., Sale, J. E., Marin, A., & Ross, L. E. (2020). Social network analysis: An example of fusion between quantitative and qualitative methods. Journal of Mixed Methods Research, 14(1), 110-124. Yousefi Nooraie, R., Warren, K., Juckett, L. A., Cao, Q. A., Bunger, A. C., & Patak-Pietrafesa, M. A. (2021). Individual-and group-level network-building interventions to address social isolation and loneliness: a scoping review with implications for COVID19. PloS one, 16(6), e0253734.

This workshop is focused on the practical application of one specific network analysis tool, UCINET (a Windows program, there is no Mac or Linux version) in completing the analysis for a particular study. Anonymized data from one of the instructors will be provided, and the workshop will walk the participants through a defined research project. The workshop will cover: Importing the data Data transformations Dealing with missing data Network Visualizations using NetDraw Exploring dyadic hypotheses Characterizing a node’s network environment Measuring structural holes and centrality Exploring overall network structure Testing hypotheses The workshop is intended for people with a basic understanding of the network concepts covered and designed to be a hands-on supplement to the "Introduction to Core Network Concepts" workshop. Participants should bring a Windows-based laptop (or any machine with a Windows emulator). Participants do not need to own UCINET for this workshop. One to two weeks prior to the workshop, participants will be sent instructors for downloading the trial version of UCINET.

This workshop introduces the many ways that social networks influence individual and network-level 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. A framework for using networks during program implementation is presented. The workshop also 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.) No software or computing requirements are needed. The workshop will be conducted by Tom Valente who has been developing and implementing network-based interventions for nearly 25 years.

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.

Exponential random graph models (ERGM) are widely used in the social sciences to examine determinants of graph structure. This 3-hour workshop will introduce attendees to mediation and moderation analysis in ERGM using the ergMargins package for R. The workshop will describe why ERGM coefficients cannot be compared between models and why coefficients for interactions—including node matching, node mixing, and other common measures of homophily and heterophily—cannot be interpreted without adjustment. Topics covered will include (1) mediation analysis, (2) moderation analysis, (3) mediation analysis when the mediator is an interaction, and (4) mediation analysis when the main effect is an interaction. We will review a range of special cases, including interactions involving both continuous and discrete variables, necessary conditions for a causal interpretation, and mediation analysis involving endogenous graph statistics. Attendees will come away from the workshop with a deeper understanding of inferential difficulties in ERGM and with knowledge on how to address each issue using ergMargins.

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; * degeneracy assessment and avoidance. Workshop length: 3 hours Max attendees: 30

Auto-logistic actor attribute models (ALAAMs) are models for analysing social influence or social contagion for cross-sectional networks, when the outcome of interest is dichotomous. If no dependencies among the outcomes of the nodes are assumed, this model reduces to logistic regression. When dependencies through the network, such as social contagion, are assumed, however, the ALAAM provides testable parameters that capture these processes. The workshop will introduce the R package “balaam”, which provides a range of different parameters for network dependencies and estimates the model using Bayesian inference. The package also provides goodness-of-fit analysis, model selection indices, as well as principled approaches for dealing with missing outcomes. Topics treated are: principles of Bayesian inference; model specification; MCMC estimation for the ALAAM; model selection; missing data analysis. Prerequisites The workshop is intended for participants who have working knowledge of quantitative analysis and experience in empirical network research. Fundamental social network analysis skills are assumed. Literature: Koskinen, J. and Daraganova, G. (2022) Bayesian analysis of social influence. Journal of the Royal Statistical Society Series A: Statistics in Society 185.4, pp. 1855–1881. Daraganova, G. & Robins, G. (2013) Autologistic actor attribute model. In: Lusher, D., Koskinen, J. & Robins,G. (Eds.) Exponential random graph models for social networks: theory, methods and applications. New York: Cambridge University Press. pp. 102–114. Daraganova, G. & Pattison, P. (2013) Autologistic actor attribute model analysis of unemployment: dual importance of who you know and where you live. In: Lusher, D., Koskinen, J. & Robins, G. (Eds.) Exponential random graph models for social networks: theory, methods and applications. New York: Cambridge University Press, pp. 237–247. ALAAM website: https://github.com/johankoskinen/ALAAM

Hyperlink prediction, a natural extension of link prediction in graphs, focuses on inferring missing hyperlinks in hypergraphs, where a hyperlink can connect more than two nodes. This technique has diverse applications across systems such as bibliometric networks, chemical reaction networks, social communication networks, and protein-protein interaction networks (among others). In this workshop, we provide a systematic and comprehensive demonstration of hyperlink prediction using Python, primarily leveraging the PyTorch library. We will explore three structural similarity-based methods (Common Neighbors, Katz Index, and Resource Allocation), a probability-based method (Node2Vec, based on random walks), and a deep learning-based method (CHESHIRE: Chebyshev Spectral Hyperlink Predictor). To evaluate the performance of hyperlink prediction, we will use a range of metrics, including F1 scores, ROC AUC, accuracy, precision, recall, log loss, and Matthews correlation coefficient—metrics widely utilized in machine learning. Additionally, we will discuss how hyperlink prediction extends to temporal hypergraphs. To compare, benchmark, and evaluate the hyperlink prediction methods, we will use a selection of well-known or randomly generated medium-sized networks. This is a hands-on computational workshop, and participants should have some prior knowledge of Python. All computations will be performed in Jupyter Notebooks, which will be made available on GitHub before the workshop.

There have been calls for system-level interventions that target the redistribution of power within communities to achieve health equity. To develop and assess the effectiveness of these interventions, there is a need for approaches that engage multiple actors within the system to identify and evaluate who has different types of power, how power operates, and how power changes over time. Participatory social network mapping approaches can be used to map the landscape of power and social capital within communities while fostering reflections on how these dynamics shape opportunities to realize health equity. This workshop will review and provide demonstrations of two participatory social network mapping approaches applied within a community-based food systems change intervention and a coalition-based opioid fatality reduction intervention. The session will begin with an overview of the methods used to frame power dynamics within these two different interventions, including terminology applied to define different types of power influencing these two health equity interventions. Next, we will practice applying these methods using egocentric social network analysis to explore connections to different sources of power among local food justice leaders and opioid-focused coalitions. We will examine how these power networks can be understood based on their trustworthiness, collaboration, and influence and how these dimensions of power influence intervention pace, equity, and efficiency. Participants will create their own power maps and explore how these can be analyzed in R. Finally, we will explore how these methods were used to guide food system changes focused on promoting nutrition equity and to inform sustainability of opioid fatality reduction strategies within diverse community settings.