Greedy algorithms for the profit-aware social team formation problem

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...

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Bibliographic Details
Published inJournal of combinatorial optimization Vol. 44; no. 1; pp. 94 - 118
Main Authors Liu, Shengxin, Poon, Chung Keung
Format Journal Article
LanguageEnglish
Published New York Springer US 01.08.2022
Springer Nature B.V
Subjects
Combinatorics
Convex and Discrete Geometry
Greedy algorithms
Mathematical Modeling and Industrial Mathematics
Mathematics
Mathematics and Statistics
Operations Research/Decision Theory
Optimization
Social networks
Teams
Theory of Computation
Greedy algorithms
Lower bounds
Social networks
Team formation
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Summary: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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ISSN:1382-6905
1573-2886
DOI:10.1007/s10878-021-00817-y