Regarding authors as samples, a mixture distribution means those samples come from different populations, namely the collaboration patterns of the authors with few collaborators and papers differ from those with many collaborators and papers.
By focusing on collaboration in dynamic environments, we explore the utilization of collaboration patterns as models for recurring high-value collaborative tasks, which can be intelligently identified, retrieved and enacted when needed.
Also, researchers that form mostly weak (or strong) ties in the social network may indicate different collaboration patterns, for example, a researcher who has many collaborators through single papers, i.e., that person has collaborated only once with many people.
From those data, we found similar transitivity and assortativity of collaboration patterns as well as the identical distribution type of collaborators per author and that of papers per author, namely a mixture of generalized Poisson and power-law distributions.
We will focus particularly on identifying, modelling, and automating collaboration patterns that increase the scale, user participation, efficiency, and automation of collaborative media creation and management activities.
Collaboration patterns are examined by analyzing the research field correlations.
Do men and women academic faculty vary in their research collaboration patterns and strategies?
This article examines their impact on collaboration patterns between collective actors in policy processes.
The interaction model from the semiotics enables distinct U-I collaboration patterns to emerge.
The evaluation criteria were task performance, collaboration patterns (especially equity of contributions), and users' subjective experience.
