Motivated by applications in online labor markets, we study the problem of forming multiple teams of experts in a social network to accomplish multiple tasks that require different combinations of skills. Our goal is to maximize the total profit of tasks that are completed by these teams subject to the capacity constraints of the experts. We study both the offline and online settings of the problem. For the offline problem, we present a simple and practical algorithm that improves upon previous results in many situations. For the online problem, we design competitive deterministic and randomized online algorithms. These are complemented by some hardness results in both settings.

受在线劳动力市场应用的启发，我们研究了在社交网络中组建多个专家团队以完成需要不同技能组合的多项任务的问题。我们的目标是在专家的能力限制下，使这些团队完成的任务的总利润最大化。我们研究了问题的离线和在线设置。对于离线问题，我们提出了一种简单实用的算法，该算法在许多情况下改进了先前的结果。对于在线问题，我们设计了竞争性确定性和随机在线算法。两种设置中的一些硬度结果对这些结果进行了补充。