We provide a half-day introduction to modelling relational events. Relational events are time-stamped events that represent actors doing things to other actors, such as sending emails, financial transactions, acts of speech, etc. We will provide a mixture of instructions, practicals, and illustrations. More information here

Material:

Intro

Football

Master class

In this presentation I will discuss my research which explores the value of the relational tools and perspectives of social network analysis for investigating the vocal and relational disempowerment of women in popular film narratives. I will explain what I mean by these terms, reviewing how the question of gendered representation in popular cinema has typically been discussed and why I think relational perspectives are important to feminist film research. I will present my methods for constructing dynamic character interaction networks from films and present results for a number of blockbuster films, reflecting on what a relational analysis can tell us that might otherwise be overlooked. In particular, I focus on two central aspects of the research: (1) I will reflect on our understanding of “female-led” in cinema and consider the way in which our gendered vocabularies for leadership conceal some of the issues in the way that Hollywood tells stories about women and power, and (2) I will explore the question of how we appropriately represent narrative texts as data in order to leverage the analytical power of network analysis while retaining the ability to speak to the dynamic processes of narrative comprehension.

Yoshi will deliver the inaugural Pip Pattison Oration on “Computational Behavioural Science: Its roots, potential, and promise“. He argues that Computational Behavioural Science provides a unified approach to understanding, explaining, predicting, and influencing complex human behaviour dynamics that will help address challenges ranging from climate change to obesity and extremism. Throughout his career, Yoshi has provided ample evidence of how this can be done, often drawing on network analysis. Now he shows us the way forward. More details are found here

Stochastic models of sociocentric networks were developed for testing hypotheses about micro-level dependencies (e.g., clustering, preferential attachment, or homophily) on the basis of empirical network data. Due to the complex nature of sociocentric networks, parameter estimates of these models are typically obtained by simulation-based inference. This opens up the possibility of using these models as simulation tools, and study emergent macro-level phenomena with them. The combination of fitting the models to empirical data sets and using them to explain macro-level outcomes renders these models powerful tools for sociological inquiry into interdependent social systems. In this presentation, the use of exponential random graph models and stochastic actor-oriented models as generative models for such networked social systems is discussed. As illustration, the case of achievement segregation in a highly competitive setting of tertiary education will be investigated, paying special attention to the relative contributions of peer influence processes and partner selection processes to the overall segregation level.

Mixed-method socio-semantic network analysis of European creative collectives

Nikita is visiting the Melbourne node of MelNet 12 through 25 August and giving a talk about using network analysis to jointly analyse semantic networks, concept usage, and social ties (abstract). This is a joint MelNet and CHDH network colloquium.

Johan Koskinen will present the paper “What makes an organisation: Bayesian aggregation of perceived organisational ties“ (abstract) in the CHDH journal club series. Drawing on cultural consensus theory and cognitive social structures he proposes a method for estimating ERGM to inter-organisational ties when we have no direct observations on these ties but have to rely on organisational representatives.