Team Formation in Social Networks
It is now recognized that the performance of an individual in a group depends not only on her own skills but also on her relationship with other members of the group. It may be possible to exploit such synergies by explicitly taking into account social network topology. We analyze team-formation in the context of a large organization that wants to form multiple teams composed of its members. Such organizations could range from intelligence services with many analysts to consulting companies with many consultants, all having different expertise. The organization must divide its members into teams, with each team having a specified list of interrelated tasks to complete, each of which is associated with a different reward. We characterize the skill level of a member for a particular task type by her probability of successfully completing that task. Members who are connected to each other in the social network provide a positive externality: they can help each other out on related tasks, boosting success probabilities. We propose a greedy approximation for the problem of allocating interrelated tasks to teams of members while taking social network structure into account. We demonstrate that the approximation is close to optimal on problems where the optimal allocation can be explicitly computed, and that it provides significant benefits over the optimal allocation that does not take the network structure into account in large networks. We also discuss the types of networks for which the social structure provides the greatest boost to overall performance.
KeywordsGreedy Algorithm Optimal Allocation Network Effect Task Allocation Small World Network
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