The goldfish package offers tools for applying statistical models to relational event data. 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 provides an introductory theoretical overview from a social science perspective, complemented by a hands-on tutorial (as time permits) on the different models implemented in the package: * Dynamic Network Actor Models (DyNAM): Investigate relational event models as an actor-oriented decision process. - rate: Actors compete to create the next relational event (Hollway, 2020). - choice: The active actor chooses the receiver of the event from among the same (Stadtfeld and Block, 2017) or a different set of nodes (Haunss and Hollway, 2023). - choice_coordination: The creation of coordination ties as a two-sided process (Stadtfeld, Hollway, and Block, 2017a), as in studies analyzing agreements between countries. * Dynamic Network Actor Models for interactions (DyNAMi): Investigate dynamics of conversation groups and interpersonal interaction in different social contexts from an actor-oriented perspective (Hoffman et al., 2020), as in studies using social sensors. - rate: Actors compete to join or leave groups. - choice: The active actor chooses the group to join. * Relational Event Models (REM): Investigate relational event models as a tie-oriented process (Butts, 2008), taking into account right-censoring (Stadtfeld, Hollway, and Block, 2017b). Prerequisites: Participants should be familiar with R and model-based statistical inference (such as logistic regression). Please bring a laptop with the R software environment, the goldfish package installed, and its dependencies. More information about the package and installation is available at: https://github.com/stocnet/goldfish References: Butts, Carter. 2008. “A Relational Event Framework for Social Action. ”Sociological Methodology 38 (1): 155–200. Haunss, Sebastian, and James Hollway. 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, Marion, Per Block, Timon Elmer, and Christoph Stadtfeld. 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, James. 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, 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. Stadtfeld, Christoph, James Hollway, and Per Block. 2017a. “Dynamic Network Actor Models: Investigating Coordination Ties Through Time.” Sociological Methodology 47 (1): 1–40. https://doi.org/10.1177/0081175017709295. ———. 2017b. “Rejoinder: DyNAMs and the Grounds for Actor-oriented Network Event Models.” Sociological Methodology 47 (1): 56–67. https://doi.org/10.1177/0081175017733457. Length: 3 hours Participants: 30

Social actors are often embedded in multiple social networks, and there is a growing interest in studying social systems from a multiplex network perspective. Consequently, there is a growing demand for analytical methods and tools for these network structures. This workshop offers a practical introduction to the multip2 R package for analyzing multiplex network data. Participants will learn the essentials of our Bayesian multiplex mixed-effects network model in the p2 (van Duijn et al., 2004) modeling framework and gain hands-on experience with the entire workflow, from data wrangling to model interpretation and assessment through a data example. The workshop will enable participants to model 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. topics includes: – 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 – Data preparation – Picking priors via prior predictive checks – 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. Expected length: 3 hr, Max attendance: 20

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.

Theoretical studies of opinion formation and evolution in social networks often focus on convergence to a set of steady-state opinions, namely asymptotic learning (with or without consensus). This is often motivated by the desire to seek an 'equilibrium' according to various definitions from statistical physics, control engineering, or economics. In many real social settings, however, it is observed empirically that opinions do not converge to a steady state; instead, they fluctuate indefinitely. This interdisciplinary workshop has three goals: (i) to introduce attendees to fundamental theoretical tools based on Bayesian inference that are suitable for modelling opinions and their evolution; (ii) to highlight some counter-intuitive yet realistic social phenomena that emerge when applying these tools; and (iii) to bring together practitioners from different knowledge domains (e.g. media studies, political science, education, artificial intelligence, social sciences, complex systems, and network sciences), who aspire to apply the tools to real-life systems. Specifically, we begin with an introduction to Bayesian statistics and belief propagation over networks. This enables us to learn the underlying tools required for modelling and analysis of opinion evolution across social networks. At this stage, we will also review some results on asymptotic learning on social networks facilitated by Bayesian inference. We then delve into a new model of opinion formation and evolution by enmeshing Bayesian learning and peer interactions. As an illustrative example, we consider a scenario where networked agents form beliefs about the political bias of a media organisation through consumption of media products, and peer pressure from political allies and opponents. To capture the multi-modal nature of opinions (individuals can hold contradictory beliefs with different levels of certainty), we model the agents' beliefs as probability distribution functions. In certain network structures, numerical simulations reveal counter-intuitive predictions, such as wrong conclusions being reached quicker with more certainty, turbulent non-convergence (some agents cannot “make up their mind” and vacillate in their beliefs), and intermittency (agents' beliefs flip between stable eras, where their beliefs do not vary over many time steps, and turbulent eras, where their beliefs fluctuate from one time step to the next). We will also consider belief disruption by partisans, i.e. stubborn agents who do not change their beliefs. If time permits, attendees will receive practical, hands-on instruction in coding the methods covered during the workshop. Workshop Length: 3 hours. Maximum Attendees: 30.

This workshop provides 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.

This workshop will provide a tutorial of advanced usage of 'ergm' and extension packages, focusing on binary networks. Topics include specifying complex structural constraints, estimation tuning, use of term operators, and observational (e.g., missing data) structure. Also included is using the new 'ergm.multi' package for modelling multilayer and multimode networks, as well as joint models of ensembles of networks. Prerequisites: Familiarity with R and 'ergm' required. If you are new to ERGMs, the introductory workshop on ERGMs using Statnet is strongly suggested.

A brief description of your workshop (max. 500 words) This workshop provides a roadmap for understanding the concept of social capital with practical tools to help organize and understand the different conceptual and theoretical approaches. It is designed to rapidly introduce the concept and its use in research, helping avoid weeks or even months of reading. It will help you quickly navigate the different meanings and conceptual approaches, directing you to the best approach for your research or interest and giving you reference lists and readings. Social capital is a complex concept with many different meanings and conceptual approaches that can be difficult and complicated to understand and apply. The literature on the topic is incredibly broad and diverse, presenting an ongoing challenge for anyone interested in using the concept in research or practice. The concept has numerous theoretical foundations, making reading the literature challenging, even for experienced scholars. Most people reading the literature report feeling confused and uncertain, and everyone can benefit from a deeper understanding of the theories of social capital. Over the last 20 years of working on the concept of social capital, Tristan Claridge, the facilitator of this workshop, has developed numerous typologies for understanding the concept. His work has sought to identify the differences and similarities of different approaches to social capital, and he has constantly asked difficult questions to explore the deeper meanings and theoretical foundations. There are no “silver bullets” and no quick simple solutions that are appropriate for every discipline and every application. But this workshop guides and directs you. Ultimately, the goal of this workshop is to help you understand the concept better, apply it more effectively, and save you time in doing so. You will come away with a deeper understanding of the concept of social capital and how to apply it in research or practice. Names and contact information of all organizers Tristan Claridge Director, Institute for Social Capital President, International Social Capital Association Email tristan@socialcapitalresearch.com Phone +61 (0)493 175 542 Length of the workshop (3 or 6 hours) 3 hours Maximum number of attendees 50

In this accessible workshop, we will provide an introduction to using the PARTNERTM CPRM (Community Partner Relationship Manager) software to collect and analyze social network data for continuous network monitoring and improvement in community settings. This platform reduces the complexity of network data collection, provides automated analysis and visualizations, and creates opportunities for community ownership and public-facing data sharing. We will highlight the key components of capturing data with PARTNERTM CPRM, including: > Member Management: Participants will learn how to populate a PARTNERTM CPRM ecosystem with a list of network members, assign attributes to those members, and include them in a data capture. We will also discuss how to build on existing approaches community networks and organizations may be using to track their members (e.g., via spreadsheet or database). > Question Design & Data Collection: Participants will learn about how to use the standard survey questions included in the PARTNERTM CPRM platform as well as how to design their own. They will also learn about how to schedule in-platform email recruitment and track responses as they come in. We will also demonstrate ways to track networks as they develop over time and to capture data about “networks of networks” using the tool. > Analysis & Dissemination: All participants will have a chance to explore the analysis tools on the platform, including network visualization, key metrics, GIS mapping, and chart/table development. Participants will also learn how to disseminate results through the platform using member profiles (individualized profiles for each member of the network) and dashboards (which can track whole network data as it comes in). Participants will leave the session with improved capacity for using PARTNERTM CPRM to: (1) design and implement community-engaged social network analysis projects, (2) track community networks over time, and (3) disseminate results of network analyses in practical contexts. Participants should bring their own computer to access workshop resources and follow along with tutorials to use the software.

The development of the social network perspective has progressed rapidly, evolving from " random pieces sitting out in the midst of the desert (forest?)” (Mullins & Mullins, 1973, p. 264) to a field of study that is " Finally, there is reason to be hopeful since the field of social network analysis is currently very “hot,” growing at an amazing rate.” (Freeman, 2004, p. 167). Much has changed since these early observations, as we will see through recent bibliometric studies (e.g., Espinosa-Rada & Ortiz, 2022; Maltseva & Batagelj, 2020, 2021, 2022, 2024). The history of social network analysis reveals key groups and institutional milestones that have driven its development and consolidation, including events like the Sunbelt conferences, the establishment of INSNA, and the launch of network-focused journals (Freeman, 2004; Scott, 2011). The field of network science (Brandes et al., 2013) has also significantly influenced this trajectory. Furthermore, we aim to contextualize phenomena such as the “physicist invasion” and the more recent “data scientist invasion,” as well as the emergence of advanced statistical models in social network research to identify the contribution of modern interdisciplinary trends. The field continues to be shaped by a vibrant community of practitioners, as illustrated in resources like the Knitting Networks podcast. By revisiting the history of the social network perspective, participants will gain insight into the origins of key concepts such as homophily, structural balance, cliques, or roles. They will also explore how different research groups have used social network approaches to address core questions in the social sciences. By understanding the field’s evolution, participants can more fully appreciate the opportunities and challenges the social network perspective faces today, leveraging historical insights to shape future research.

This workshop is about analysing mobility networks, that is, networks in which nodes represent locations and ties are individuals that are mobile between these locations. Examples of mobility networks include migration of individuals between countries and mobility of workers between organisations. Mobility networks as understood here are directed and weighted. The workshop teaches a statistical method to analyze such data, which is introduced in “Block, P., Stadtfeld, C., & Robins, G. (2022). A statistical model for the analysis of mobility tables as weighted networks with an application to faculty hiring networks. Social Networks, 68, 264-278.”. The method is implemented in MoNAn, a package of the statistical system R. The workshop will demonstrate the basics of using MoNAn. Attention will be paid to the underlying statistical methodology, to examples, and to the use of the software. The goal of this method is to model endogenous (network) patterns in mobility networks, such as concentration, reciprocation, and triadic clustering. The prevalence of these endogenous structure can be modelled alongside classical predictors of mobility that concern attributes of individuals and locations (i.e., “controlling for” these predictors). As such, it is in the spirit of ERGMs but applies to mobility data. Technically, the presented model represents an extension of classical log-linear models applied to mobility tables. 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 a deeper treatment of the statistical model and detailed introduction into some advanced features of the software, for example, goodness of fit, or advanced model specification. A basic introduction of the software and pointers to further material is provided on the MoNAn github page (github.com/stocnet/MoNAn). Prerequisites: Course participants should have a basic understanding of model-based statistical inference (say, logistic regression), some prior knowledge of social networks, and should have had some basic exposure to the R statistical software environment. They are expected to bring their own laptop to the course (Windows, Mac or Linux), with the R statistical software environment and the MoNAn package pre-installed. Participants for whom R is new are requested to learn the basics of R before the workshop: how to run R and how to give basic R commands. This is to reduce the amount of new material to digest at the workshop itself. Further instructions will be given before the conference starts. Organiser: Per Block, University of Zurich, Department of Sociology. email: per.block@uzh.ch Workshop length: 6 hours. Max Participants: 30

The structure of the network N=(V, L, W, P) is determined by the graph G=(V, L), where V is the set of nodes and L is the set of links. In addition, additional data about links (weights from W) and nodes (properties from P) are often known. The network N is weighted if its set of weights is nonempty. The weights can be either measured (such as trade networks - BACI/CEPII https://www.cepii.fr/CEPII/en/bdd_modele/bdd_modele_item.asp?id=37) or computed (for example a projection of a two-mode network). The workshop will cover the following topics: - examples and sources of weighted networks, - transformations of weighted networks (dealing with large ranges of values of weight, making nodes comparable, Balassa index), - visualization of weighted networks (graph drawing, monotonic recoding, matrix representation, ordering of nodes), - clustering and blockmodeling. - important nodes - hubs and authorities, - skeletons - important parts of the network: cuts, k-neighbors, Pathfinder, cores, trusses, backbone, islands, - temporal weighted networks. Most of the topics are discussed in the book Batagelj, Doreian, Ferligoj, Kejz̆ar (2014) Understanding Large Temporal Networks and Spatial Networks. The workshop is based on the programming system R https://cran.r-project.org/. The network data and additional R code will be available on GitHub https://github.com/bavla/Nets/tree/master/ws .

Leveraging the power of big data represents an opportunity for researchers and managers to reveal patterns and trends in social and organizational behaviors. This workshop demonstrates how to successfully integrate Text Mining with Social Network Analysis for business and research applications. It introduces the Semantic Brand Score (SBS) and other advanced methods and tools for analyzing semantic networks, assessing brand/semantic importance, and performing complex NLP tasks. Participants will also learn about network topic models and methods for measuring language novelty and impact, among other key techniques. The workshop highlights the functionalities of the SBS Business Intelligence App (SBS BI), which is designed to produce a wide range of analytics and mine textual data. Through several case studies, we show how these methods have been used, for example, to predict tourism trends, select advertising campaign testimonials, and make economic, financial, and political forecasts. SBS BI's analytical power extends beyond "brands", with applications that include: commercial brands (e.g., Pepsi vs. Coke); products (e.g., pasta vs. pizza); personal brands (e.g., the name and image of political candidates); and concepts related to societal trends (e.g., terms used in media communication that shape public perceptions of the economy). By combining text analysis with network science, the workshop equips participants with tools that can transform decision-making and organizational management in the era of big data. More info and materials are available at: https://learn.semanticbrandscore.com

Advances in information technology have increased the availability of time-stamped relational data such as those produced by email exchanges or interaction through social media. Whereas the associated information flows could be aggregated into cross-sectional panels, the temporal ordering of the interactions frequently contains information that requires new ideas for the analysis of continuous-time interactions, subject to both endogenous and exogenous influences. The Relational Event Model (REM) has turned out to be a versatile framework that has allowed further methodological extensions to address a multitude of applied demands: how to deal with non-linear and time-varying effects, how to account for network heterogeneity, how to analyze relational hypergraphs, how to address goodness-of-fit. In this short course, we introduce the REM, define its core properties, and discuss why and how it has been considered useful in empirical research. Then we will focus on how new applications have pushed the development of relational event modelling forward. 1. Introduction to REMs If a process consists of a sequence of temporally ordered events involving a sender and a receiver, such as email communication or bank transactions, the REM can be used to identify drivers of this process. It is based on event history modelling, in particular the Cox model, which allows for convenient and efficient estimation. 2. Mixed effect additive REMs We show how to extend REM formulations with non-linear specifications of endogenous effects, as well as time-varying influence of covariates on the event rate. We explain how the incorporation of random effects can uncover latent heterogeneity associated with individual actors or groups of them. Furthermore, we will describe a general method to assess the goodness-of-fit of such models. 3. Modelling relational hyperevents We will discuss "polyadic" social interaction processes in which events can connect varying and potentially large numbers of nodes simultaneously. Examples of such polyadic events (or "hyperevents") include meeting events or social gatherings, multicast (i.e., "one-to-many") communication events such as emails in which one actor sends the same message to several receivers, co-offending, or scientific collaboration (e.g. co-authoring and citation networks). The workshop will feature a mix short explanatory sessions with hands-on computer practicals. Participants are encouraged to bring their own laptop with Rstudio pre-installed. The workshop is targeted at participants interested in statistical modelling of networks based on relational event data, with a specific focus on non-linear, time-varying and random effects, and polyadic interaction events. The software eventnet together with R-package mgcv will be explained and used in the context of this tutorial. Additional reading material will be made available to the participants beforehand.

This workshop will explore the potential of agent-based modelling (ABM) to address complex social challenges. Through a combination of theoretical foundations and hands-on exercises, participants will develop practical skills in designing, implementing, and analysing ABMs across various domains of social impact. ## Morning Session (3 hours) Foundations (1.5 hours) x ABM fundamentals and integration with social network analysis x Key concepts: emergence, interaction networks, behavioural rules x Case studies: public health interventions, educational outcomes, environmental behaviour change Tools and Implementation (1.5 hours) x Introduction to NetLogo and Python's Mesa framework x Setting up simulation environments x Basic model development ## Afternoon Session (3 hours) Applications and Practice (2 hours) x Hands-on exercises with real-world scenarios x Model building in small groups x Implementation strategies and best practices Validation and Analysis (1 hour) x Model validation techniques x Results interpretation x Documentation and sharing x Resources for advanced modelling

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

INSTRUCTOR: Alberto Caimo, University College Dublin, Ireland CRAN: https://CRAN.R-project.org/package=Bergm WEBSITE: http://acaimo.github.io/Bergm SUMMARY: Bayesian analysis is a promising approach to social network analysis because it yields a rich fully probabilistic picture of uncertainty which is essential when dealing with relational data. Using a Bayesian framework for exponential random graph models (ERGMs) leads directly to the inclusion of prior information about the network effects and provides access to the uncertainties by evaluating the posterior distribution of the parameters. The growing interest in Bayesian ERGMs can be attributed to the development of very efficient computational tools developed over the last decade. This hands-on workshop will provide participants with the opportunity to acquire essential knowledge of the main characteristics of Bayesian ERGMs using the Bergm package for R. TOPICS: – Brief overview of ERGMs; – Intro to Bayesian analysis; – Prior specification; – Model fitting and model selection; – Interpretation of model and parameter posterior estimates; – Model assessment via goodness-of-fit procedures. The workshop will have a strong focus on the practical implementation features of the software that will be described by the analysis of real network data. Interactive material will support the acquisition of concepts and understanding of the tutorial through code, scripts, and documentation. PREREQUISITES: Basic knowledge of social network analysis and R. Participants are recommended to bring a laptop with R/RStudio, and Bergm installed. REFERENCES: Caimo, A., Bouranis, L., Krause, R., and Friel, N. (2022) “Statistical Network Analysis with Bergm.” Journal of Statistical Software, 104(1), 1–23.

Stochastic Actor-oriented Models (SAOMs), as implemented in RSiena, are statistical models for analysing network dynamics. SAOMs assume that you have observed the network at at least two points in time. These models have been extended to handle many forms of longitudinal networks and could be said to collectively be regarded as the gold standard methods for such data. Having observations on multiple networks, multilevel networks, is becoming increasingly common. This workshop deals with longitudinal analysis of such multilevel models, in particular the random coefficient multilevel longitudinal network analysis implemented in the function sienaBayes which is part of multiSiena, the sister package of RSiena. This method is based on the Stochastic Actor-oriented Model (SAOM). The basic idea of this random coefficient model will be presented, with the approach taken by the analysis using sienaBayes. The use of this function will be explained, and guidance will be given for parameter interpretation. Topics treated are: principles of Bayesian inference; the random coefficient multilevel version of the SAOM (ML-SAOM); MCMC estimation of the ML-SAOM; operation of sienaBayes; parameter interpretation. Prerequisites The workshop is intended for participants who know about the Stochastic Actor-oriented Model, and have practical experience in working with RSiena. Literature: Ripley, Ruth M., Tom A.B. Snijders, Zsofia Boda, Andras Voros, and Paulina Preciado (2023). Manual for RSiena. URL: https://www.stats.ox.ac.uk/~snijders/siena/RSiena_Manual.pdf Koskinen, Johan H. and Tom A.B. Snijders (2023). Multilevel longitudinal analysis of social networks. Journal of the Royal Statistical Society, Series A. DOI: https://doi.org/10.1093/jrsssa/qnac009 SIENA website: http://www.stats.ox.ac.uk/~snijders/siena Maximum number of participants 30.

Net-Map is an interview-based mapping tool that helps people understand, visualize, discuss, and improve situations in which many different actors influence outcomes. It is an innovative, analogue and accessible approach to achieving results in complex projects where many different interests influence the result. By creating Influence Network Maps, individuals and groups can clarify their own view of a situation, foster discussion, and develop a strategic approach to their networking activities Social and organizational change often involves diverse actors outside a single hierarchy, making it challenging to coordinate actions through mandates alone. Quantitative Social Network Analysis (SNA) has been widely applied to explore these dynamics, yet Net-Map is a mixed-methods approach that enables participative collection of both qualitative and quantitative data. Net-Map moves beyond understanding network structure to reveal the “how” and “why” behind relationships and engages participants in mapping their networks, discussing opportunities and challenges, and working toward a shared vision. Net-Map has increasingly been adopted by practitioners in action research, communication strategy, advocacy, political networks, social movements, program implementation, personal development, project management, organization learning, and change management. For instance, it has been employed for strategizing government reforms in Zimbabwe, improving business relationships between fortune 500 companies, supporting personal transformation through network coaching or empowering grassroots political change movements. • A hands-on workshop: During the workshop, participants will be introduced to the Net-Map process and will practice drawing a multiplex network while also identifying perceived levels of influence of actors. Participants will indicate the actors’ goals and have an in-depth discussion about the situation. This workshop will help users to answer questions such as: Do you need to strengthen the links to an influential potential supporter (high influence, same goals)? Do you have to be aware of an influential actor who doesn’t share your goals? Can increased networking help empower your dis-empowered beneficiaries? By creating Influence Network Maps, individuals and groups can clarify their own view of a situation, foster discussion, and develop a strategic approach to their networking activities. More specifically, Net-Map will help participants to determine : – what actors are involved in a given network, – how they are linked, – how influential they are, and – what their goals are You can learn more about the technique here – http://netmap.wordpress.com/about/ • Workshop format This is a 3-hour workshop; the maximum number of attendees is 20. After a brief introduction to the approach, the participants will map actual or potential cases to experience the use of Net- Map. We will discuss these cases identifying benefits and limitations, feasibility and requirements of this approach. • Instructors Amitaksha Nag – As a systems change expert focused on facilitating collective learning in complex systems, he utilizes participatory and group modeling approaches to drive impactful outcomes. He developed the Datamuse Network Analysis tool, which has advanced the online application of Net-Map for network analysis and visualization. Ana Elia Ramon Hidalgo – Certified Net-Map trainer and currently works as an independent consultant in organizational development. She holds a PhD in Social Networks in Community- Based Natural Resources Management.

In large networks with thousands or millions of actors, the interactions among actors are not affected by the interactions among all other actors, because many social networks are more local than global in nature: Indeed, actors may not even know most other actors, and therefore cannot be influenced by them. A simple class of models that respects the local nature of many social networks assumes that actors are divided into communities and that actors are affected by other actors of the same community, but are not affected by actors outside of the community. The communities may be known or unknown. If the communities are unknown, one can infer the unobserved communities from the observed social network along with the social forces that govern interactions among actors within and between communities. The proposed workshop focuses on next-generation ERGMs for large networks implemented in R package bigergm, which is an evolution of R packages hergm and lighthergm. The workshop will introduce the basic ideas of next-generation ERGMs and will demonstrate them by examples. Participants will be provided with sample R scripts. Software: Fritz, Schweinberger, Komatsu, Dahbura, Nishida, and Mele (2024). R package bigergm. https://cran.r-project.org/web/packages/bigergm/index.html Literature: The basic idea is introduced in Schweinberger and Handcock (2015). Local dependence in random graph models: Characterization, properties and statistical inference. Journal of the Royal Statistical Society, Series B, 77, 647-676. An application to systemic risk in social networks can be found in Fritz, Georg, Mele and Schweinberger (2024). Vulnerability webs: Systemic risk in software networks. Computational details are provided in Babkin, Stewart, Long, and Schweinberger (2020). Large-scale estimation of random graph models with local dependence. Computational Statistics & Data Analysis, 152, 1-19.

Modeling the dynamics of infectious diseases on networks has a long history, and has become more prominent in recent years. This workshop provides a hands-on tutorial for the use of the R package EpiModel for network modeling of epidemics. EpiModel builds on the statnet suite of packages, especially tergm and other packages for temporal network modeling. Thus, familiarity with the concepts and methods of tergms, especially model terms, is important, although we will provide a rapid refresher. Familiarity with the basic concepts of epidemic modeling is also helpful. Familiarity with R is essential. We will cover: - An overview of the EpiModel framework - A rapid refresher on ergms and tergms - A rapid overview of epidemic modeling concepts - Specification and parametrization of models from egocentric network data - Specification and parametrization of models from complete network data - Visualization of network and epidemic outcomes - Hands-on examples for basic models - Introduction to the EpiModel API to expand models beyond the basics - Pointers to research-level models with published EpiModel code

The goal of this workshop is to lower the barriers of using geographic information systems (GIS) and geospatial mapping in social network analysis, and to explain what is possible with GIS and mapping in SNA. Participants will learn to put social networks on maps and answer basic questions such as: Do nodes with high closeness centrality cluster together? Do different communities overlap in geographic space? Which places have mostly local or distant ties? Which nodes have the closest or most distant connections? How many nodes are in a certain part of the study area? Which nodes are spatial outliers? Which nodes are nearby but very disconnected? Which edges cross administrative units or natural features? We will use a free, open-source, web-based tool called the Social Network Mapping Analysis (SNoMaN) for exploratory spatial data visualization (ESDA) in research and classroom use. Participants will explore case studies of a networks of social impact organizations, GitHub collaborations, a U.S. Congressional network of vote agreements, spatial actor-movie networks, examples in published literature, and other examples of geographic node-edge structures. They will learn to plot nodes and edges on a map, filter by geographic selection, and stylize the map based on factors of interest such as node degree, edge distance, node type, cluster, etc. They will learn how to use cutting-edge visualization methods such as cluster-cluster plots, centrality-centrality plots, route factor diagrams, and perform a spatial cluster detection of network communities. They will also explore newly published optimization-based statistics such as k-fulfillment, and local and global network flattening ratios, as well as geo-based methods such as average nearest neighbor (ANN) clustering and spatial modularity detection analysis. Participants will interactively compute and visualize spatial social network metrics, describe spatial distributions, explore associations, and learn to detect anomalies. SECTIONS OF THE WORKSHOP Introduction and demonstration: We will introduce basic concepts behind mapping a social network (e.g., how to pin your nodes to a location). Then we will do a demonstration/tutorial on the Social Network Mapping Analysis (SNoMaN) software and its functionality. Hands-on guided session: This will be a hands-on guided analysis with directions, where participants can navigate the software to generate insights. We will encourage participants to pair up or work in small groups. The leader will assist participants and encourage interaction between pairs of participants. Open exploration: Participants will get help formatting and exploring their own social network data, or use a built in dataset, with the SNoMaN tool. Open mic session: During this session, participants will be invited to show the insights they derived about their own spatial social network data or their own exploration. Closing thoughts: Participants can share thoughts or ideas with the group and how they may incorporate geographic space and GIS into their social network analysis in the future. No experience or preparation necessary. This workshop is suitable for geo-beginners. We encourage participants to bring a laptop to this workshop to get the most out of the hands-on activities.

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

People tend to use a variety of desktop or web tools such as Gephi to practice visual network analysis. Unfortunately, It often means being forced to work on the graph's data in separate tools, such as spreadsheets or processing them using programming languages. This makes the feedback loop between data wrangling and visualisation a bit tedious. On the other hand, the scientific community now has access to fantastic tools such as Jupyter notebooks, able to mix interactive programming and visualizations seamlessly. So why not use this new medium to also perform visual network analysis? This is exactly what the "ipysigma" Jupyter widget, developed at SciencesPo médialab, intend to do. ipysigma is a powerful tool that renders an interactive view of a graph directly in a notebook cell. It lets you zoom and pan the graph to explore it fully. You can also search & filter nodes, node categories and edges, apply a real-time animated 2d layout algorithm, all while remaining able to customize a large variety of the graph's visual variables: node and edge sizes, color, borders, halos, being just the most basic examples. It is notably relying on the sigma.js library, using WebGL, to make sure it can display large graphs in a web browser, which is not the case with most other graph rendering engines. In this workshop, participants will learn how to leverage the widget to perform their visual network analysis, through typical use-cases ranging from lexicometry to webmining, all while being able to process the graph data itself in python, using a graph processing library such as networkx or igraph. Participants are therefore expected to have some basic knowledge of python and Jupyter notebooks.

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) generalised to valued ties, emphasising 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, visualisation, modelling, simulation, and analysis of network data. Prerequisites: Familiarity with R and ‘ergm’ required. If you are new to ERGMs, the introductory workshop on ERGMs using Statnet is strongly suggested.

The goal of this workshop is to provide participants an orientation to conducting personal networks research within Network Canvas and the opportunity to master the skills necessary to apply these tools within their specific domain of interest. Network Canvas (http://www.networkcanvas.com) is a free and open-source software suite that facilitates the collection of self-reported social network data, comprised of applications to support both in-person interviewer-assisted environments as well as remote self-administered studies. In this workshop, we will provide an overview of Architect, the Network Canvas visual survey builder, as well as Interviewer, the Network Canvas app used to collect data directly from participants within an in-person research design. We will also provide an overview of Fresco, the newest Network Canvas tool designed for remote network surveying. Finally, we will explore data export in Interviewer and Fresco, and a brief orientation to analysis. Expect the opportunity to engage in hands-on exercises during the session with assistance from our team. When completed, you will acquire the skills to: Design an egocentric or whole network survey Deploy and manage a study, whether in-person or remote Obtain study data in Interviewer and Fresco, and export it for analysis

The netdiffuseR package provides tools for analyzing and simulating diffusion of innovations and contagion processes on networks. In this workshop, we demonstrate the package’s features by analyzing empirical and simulated data on the diffusion of innovations. The session will include examples of using netdiffuseR jointly with other network analysis packages such as RSiena, statnet, and igraph. NetdiffuseR's main features are computing network exposure models based on weight matrices (direct ties, structural equivalence, attribute-weighted, etc.), thresholds, infectiousness and susceptibility. The package works with both static and dynamic networks. Some other capabilities include handling relatively large graphs, simulating networks and diffusion of innovation processes, and visualizing the diffusion of innovations. While there are no prerequisites, it is suggested that you have a working knowledge of the R programming language. While there are no prerequisites, it is suggested that you have a working knowledge of the R programming language. We will use the latest version of the netdiffuseR R package, which can be found on GitHub here: https://github.com/USCCANA/netdiffuseR. During the workshop day, we will provide access to a cloud version of RStudio with the latest version of netdiffuseR, so do not worry if you cannot install the package before the workshop. Duration: 3 hours Max participants: 30

A significant proportion of the population enters adulthood having already faced mental health challenges. These issues during adolescence have enduring effects on health, education, and socio-economic outcomes throughout life. Current approaches to adolescent mental health often fail to capture the intricate social and emotional contexts of young people. Traditional methods tend to focus on individuals in isolation, neglecting the broader social networks that play a crucial role in mental health. Unsituated social network analysis may fail to account for how relationships vary across time and space within social settings. This workshop will introduce the SOCially sITuated Systems (SOCITS) approach to measuring and modelling adolescent mental health. The methodology was developed with a focus on stress and loneliness in schools, but the approach can be applied to other constructs, behaviours and social settings. SOCITS integrates qualitative, systems thinking workshops, Agent Based Modelling and quantitative survey data; taking a co-production approach with young people and school staff. Qualitative and workshop data are used to inform the development of rules for an Agent Based Model, and also to develop situated survey items that are tailored to the places, interactions, and social situations that are relevant to specific schools and the topic of interest. The workshop will introduce participants to the conceptual integration of complexity theory and situated cognition theory; outline approaches for study design; and provide an overview of the range of analytical options, R packages, tutorial datasets and scripts available when analysing situated cognition and situated network data. Workshop learning objectives: 1. Introduce participants to relevant concepts and theories underpinning the SOCITS approach. 2. Explore qualitative methods that can be employed in identifying stressful situations in schools and ways that these can be improved. 3. Demonstrate how workshop methods can be applied to co-produce situated assessment and social network survey items. 4. Apply agent-based models to model social and spatial dynamics influencing wellbeing in schools.

From infectious diseases to innovations, from policies to norms, networks often influence how outcomes are distributed. This workshop introduces the many analytic and visualisation tools available in the ‘manynet’, ‘migraph’ and associated packages in R for studying network diffusion, contagion, or learning. First, we look at the tools available for simulating various contagion processes, including simple and complex diffusion. We extend these models to a range of different compartment models, e.g. SEIRS, that can better represent more varied processes, and suggest how to evaluate the fit of these simulations with observed diffusions. Second, we look at tools for measuring, describing, or inferring aspects of these processes, from hazard rates to thresholds. We show how they can be used on observed diffusions too, so please bring data from any salient projects you are working on. Lastly, we will explore how, with the rest of the tools available in ‘manynet’ and ‘migraph’, we can identify points of intervention to accelerate or obstruct diffusion. Because these procedures are based on ‘manynet’, they work with many different network formats and types, including ‘igraph’ and ‘network’ classes, as well as directed, multimodal, signed and multiplex data. Familiarity with R and RStudio is recommended.

This workshop introduces epiworldR, an R package with a fast (C++ backend) and highly customizable framework for building network-based transmission/diffusion agent-based models (ABM). These models provide valuable information that may aid in performing complex simulation studies and make informed, evidence-based policy decisions for the general population. epiworldR is a flexible tool that can capture the complexity of transmission/diffusion dynamics resulting from agents’ heterogeneity, network structure, transmission dynamics, environmental factors (e.g., policies), and many other elements. Some key features of epiworldR are the ability to construct multi-disease models (e.g., models of competing multi-pathogens/multi-rumor), design mutating pathogens, architect population-level interventions, and build models with an arbitrary number of compartments/states (beyond SIR/SEIR). Moreover, epiworldR is really fast. For example, simulating a SIR model with 100,000 agents for 100 days takes less than ⅓ of a second (about three times faster than most popular packages). The workshop will be 100% hands-on. It will feature examples of simulating multi-disease/rumor models, policy intervention models, and mutating variants. You can learn more about what to expect by visiting https://uofuepibio.github.io/epiworldR-workshop/. Participants should have a working knowledge of R (e.g., some experience with statnet). We will be using the latest version of epiworldR and will also provide a cloud environment with all the required components for the workshop. Duration: 3 hours Maximum number of attendees: 30