  Collaborative teamwork is key to major scientific discoveries. However, the
prevalence of collaboration among researchers makes team recognition
increasingly challenging. Previous studies have demonstrated that people are
more likely to collaborate with individuals they are familiar with. In this
work, we employ the definition of familiarity and then propose MOTO
(faMiliarity-based cOllaborative Team recOgnition algorithm) to recognize
collaborative teams. MOTO calculates the shortest distance matrix within the
global collaboration network and the local density of each node. Central team
members are initially recognized based on local density. Then MOTO recognizes
the remaining team members by using the familiarity metric and shortest
distance matrix. Extensive experiments have been conducted upon a large-scale
data set. The experimental results show that compared with baseline methods,
MOTO can recognize the largest number of teams. The teams recognized by MOTO
possess more cohesive team structures and lower team communication costs
compared with other methods. MOTO utilizes familiarity in team recognition to
identify cohesive academic teams. The recognized teams are in line with
real-world collaborative teamwork patterns. Based on team recognition using
MOTO, the research team structure and performance are further analyzed for
given time periods. The number of teams that consist of members from different
institutions increases gradually. Such teams are found to perform better in
comparison with those whose members are from the same institution.
