Team formation plays an essential role in the labor market. In this paper, we propose two bicriteria algorithms to construct a balance between gain and cost in a team formation problem under the streaming model, subject to a cardinality constraint. We formulate the problem as maximizing the difference of a non-negative normalized monotone submodular function and a non-negative linear function. As an extension, we also consider the case where the first function is \(\gamma \)-weakly submodular. Combining the greedy technique with the threshold method, we present bicriteria streaming algorithms and give detailed analysis for both of these models. Our analysis is competitive with that in Ene’s work.

团队组建在劳动力市场中起着至关重要的作用。在本文中，我们提出了两种双准则算法，以在流模型下的团队组建问题中构建收益和成本之间的平衡，受基数约束。我们将问题表述为最大化非负归一化单调子模函数和非负线性函数的差异。作为扩展，我们还考虑第一个函数是\(\gamma \)弱子模的情况。将贪婪技术与阈值方法相结合，我们提出了双标准流算法，并对这两种模型进行了详细分析。我们的分析与 Ene 的工作具有竞争力。