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.