Algorithms for team formation based on the degree distribution of the social networks

Abstract

The problem of choosing a team for a given project/task with minimum communication cost is known as team formation problem for which many algorithms have been proposed in the literature. The skill-centric algorithms in the literature start by searching for suitable experts for each skill. These algorithms are very slow as the search requires shortest path calculations. We propose that by considering the topology of the underlying social network, the algorithms can be made more efficient. We contribute two algorithms in this paper for team formation, namely, TPLRandom and TPLClosest which exploit the power law of the degree distribution of the social network to form a team. The proposed algorithm is based on the idea that is generally adopted while a team is being formed for the real world challenges. A team leader is identified first who then sets about choosing the team members possessing the necessary skills required for the task. The algorithms choose high degree nodes from the heavy tail of the degree distribution to act as leaders. The leaders form teams from their own neighbourhoods and the one with the lowest communication cost is chosen as the best team. This is an entirely novel approach to team formation problem. We show that these high degree experts and their neighbours cover a large number of skills required for the task, reducing the expensive computations and thus yielding a fast and scalable algorithm. The experimentation is carried out on the well-known DBLP data set. We build a much larger benchmark data set from DBLP for experimentation. Our algorithms TPLClosest and TPLRandom provide teams with significantly lower communication costs. They also surpass the other conventional algorithms such as MinLD and MinSD in terms of the execution time.