  In an organization, tasks called projects that require several skills, are
generally assigned to teams rather than individuals. The problem of choosing a
right team for a given task with minimal communication cost is known as team
formation problem and many algorithms have been proposed in the literature. We
propose an algorithm that exploits the community structure of the social
network and forms a team by choosing a leader along with its neighbours from
within a community. This algorithm is different from the skill-centric
algorithms in the literature which start by searching for each skill, the
suitable experts and do not explicitly consider the structure of the underlying
social network. The strategy of community-based team formation called TFC leads
to a scalable approach that obtains teams within reasonable time over very
large networks. Further, for one task our algorithms TFC-R and TFC-N generate
multiple teams from the communities which is show-cased as a case-study in the
paper. The experimentation is carried out on the well-known DBLP data set where
the task is considered as writing a research paper and the words of the title
are considered as skills. Team formation problem is translated to finding
possible authors for the given paper, who have the required skills and having
least communication cost. In the process, we build a much larger bench-mark
data set from DBLP for team formation for experimentation. Even though the
benchmark algorithm Rarestfirst takes least time, our algorithms TFC-N and
TFC-R give much better communication cost. They also outperform the standard
algorithms like MinLD and MinSD with respect to the time taken in finding a
team. The time taken by our algorithms on communities are several orders faster
than the time taken on the larger network without compromising too much on the
communication cost.
