  In a team formation problem, one is required to find a group of users that
can match the requirements of a collaborative task. Example of such
collaborative tasks abound, ranging from software product development to
various participatory sensing tasks in knowledge creation. Due to the nature of
the task, team members are often required to work on a co-operative basis.
Previous studies have indicated that co-operation becomes effective in presence
of social connections. Therefore, effective team selection requires the team
members to be socially close as well as a division of the task among team
members so that no user is overloaded by the assignment. In this work, we
investigate how such teams can be formed on a social network.
  Since our team formation problems are proven to be NP-hard, we design
efficient approximate algorithms for finding near optimum teams with provable
guarantees. As traditional data-sets from on-line social networks (e.g.
Twitter, Facebook etc) typically do not contain instances of large scale
collaboration, we have crawled millions of software repositories spanning a
period of four years and hundreds of thousands of developers from GitHub, a
popular open-source social coding network. We perform large scale experiments
on this data-set to evaluate the accuracy and efficiency of our algorithms.
Experimental results suggest that our algorithms achieve significant
improvement in finding effective teams, as compared to naive strategies and
scale well with the size of the data. Finally, we provide a validation of our
techniques by comparing with existing software teams in GitHub.
