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

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

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.