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.

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.

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.