Abstract
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.
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Acknowledgements
The first and second authors are supported by National Natural Science Foundation of China (No. 11871081) and Bei**g Natural Science Foundation Project No. Z200002. The third author is supported by National Natural Sciences and Engineering Research Council of Canada (NSERC) grant 06446, and National Natural Science Foundation of China (Nos. 11771386, 11728104). The fourth author is supported by National Natural Science Foundation of China (No. 11801251).
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A preliminary version appeared in the Proceedings of the 9th International Conference on Computational Data and Social Networks, 2020, pp. xiii–xiv.
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Wang, Y., Xu, D., Du, D. et al. Bicriteria streaming algorithms to balance gain and cost with cardinality constraint. J Comb Optim 44, 2946–2962 (2022). https://doi.org/10.1007/s10878-021-00827-w
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DOI: https://doi.org/10.1007/s10878-021-00827-w