Capacitated Team Formation Problem on Social Networks

TL;DR

This paper addresses the NP-hard problem of forming effective, socially close teams on social networks for collaborative tasks, proposing efficient approximation algorithms and validating them on large-scale GitHub data.

Contribution

It introduces novel approximation algorithms with provable guarantees for the capacitated team formation problem on social networks.

Findings

Algorithms outperform naive strategies in effectiveness

Scales efficiently with large datasets

Validated by comparison with real GitHub teams

Abstract

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.