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 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.

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

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.