will give a presentation on
Network separation measures the likelihood of connections within groups compared to those between groups, operating at both the network and node levels. This concept holds significance across various disciplines such as sociology, management, and social psychology, providing insights into social inclusion, inequalities, discrimination, stereotyping, norms enforcement, and conflicts.
The separation can be assessed across different attributes, including time-invariant factors such as gender and ethnicity, as well as time-variant factors like achievement and smoking status.
This talk aims to showcase the importance of measuring separation in network analysis. It will delve into various metrics, discussing their advantages and disadvantages. Additionally, the session will provide practical insights into conducting these analyses using the R package Netseg by Michal Bojanowski. The presentation will be enriched with real-world applications, drawing examples from the SILNER and EGONET studies.
The seminar will be followed by a coffee break.