Information on 25 actors of a consultancy firm for which a sequence of e-mail messages is observed (can be accessed through the 'events' data object). The actor data is simulated based on information provided in Mulder & Leenders (2019). In the original data, 70 actors were involved. The current data is a random sample of 25 actors.
data(actors)
dataframe (25 rows, 4 columns)
actors$id | integer |
ID of the employee, corresponding to the sender and receiver IDs in the events dataframe |
actors$position | numeric |
Hierarchical position of the employee, ranging from 1-4 |
actors$division | character |
Categorical variable, indicating the division of the employee |
actors$location | integer |
Categorical variable, indicating the location of the building the employee works in |
The related data files 'events', 'same_building', 'same_division' and 'same_hierarchy' contain information on the event sequence and three event statistics respectively.
Mulder, J., & Leenders, R. T. (2019). Modeling the evolution of interaction behavior in social networks: A dynamic relational event approach for real-time analysis. Chaos, Solitons and Fractal Nonlinear, 119, 73-85, https://doi.org/10.1016/j.chaos.2018.11.027 doi:10.1016/j.chaos.2018.11.027