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Greedy algorithms for the profit-aware social team formation problem

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Abstract

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.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

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Acknowledgements

The authors would like to thank the anonymous referees for their useful comments on earlier versions of the manuscripts. The second author would like to thank the support of the Deep Learning and Cognitive Computing Centre of The Hang Seng University of Hong Kong.

Funding

This work was partially supported by the National Natural Science Foundation of China under grant 62102117.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China

    Shengxin Liu

  2. Department of Computing, The Hang Seng University of Hong Kong, New Territories, Hong Kong

    Chung Keung Poon

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  1. Shengxin Liu
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  2. Chung Keung Poon
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Correspondence to Chung Keung Poon.

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This paper was presented in part at the International Conference on Combinatorial Optimization and Applications (COCOA) 2017 (Liu and Poon 2017).

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Liu, S., Poon, C.K. Greedy algorithms for the profit-aware social team formation problem. J Comb Optim 44, 94–118 (2022). https://doi.org/10.1007/s10878-021-00817-y

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