Abstract The development and application of models, which take the evolution network dynamics into account, are receiving increasing attention. We contribute to this field focus on a profile likelihood approach model time-stamped event data for large-scale dynamic network. investigate collaboration inventors using EU patent data. As we consider submission joint explore driving forces between in...

In this paper, we focus on inferring social roles in conversations using information extracted only from the speaking styles of the speakers. We model the turn-taking behavior of the speakers with dynamic Bayesian networks (DBNs), which provide the capability of naturally formulating the dependencies between random variables. More specifically, we first explore the usefulness of a simple DBN, n...

This paper contributes to a theory-grounded methodological foundation for automatic collaborative learning process analysis. It does this by illustrating how insights from the social psychology and sociolinguistics of speech style provide a theoretical framework to inform the design of a computational model. The purpose of that model is to detect prevalence of an important group knowledge integ...

In this paper, air combat simulation data is reconstructed into a dynamic Bayesian network. It gives a compact probabilistic model that describes the progress of air combat and allows efficient computing for study of different courses of the combat. This capability is used inwhat-if type analysis that investigates the effect of different air combat situations on the air combat evolution and out...

One common problem in the construction industry is project cost overrun. Cost overrun can have significant impacts on financial profitability, completion, quality, and stakeholder satisfaction. The average percentage of vary widely depending type, size, complexity, location. Many approaches be adopted to prevent or mitigate overrun, one them a more accurate estimate prediction. Several studies ...