Forming Diverse Teams Based on Members’ Social Networks: A Genetic Algorithm Approach

Archan Das; Diego Gómez-Zará; Noshir Contractor

doi:10.1007/978-3-030-65351-4_28

Forming Diverse Teams Based on Members’ Social Networks: A Genetic Algorithm Approach

Archan Das, Diego Gómez-Zará^*, Noshir Contractor

^*Corresponding author for this work

Industrial Engineering and Management Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Previous research shows that diverse teams in background and skills can outperform homogeneous teams. However, people often prefer to work with others who are similar and familiar and fail to assemble teams with high diversity levels. We propose a team formation algorithm that suggests diverse teams based on individuals’ social networks, allowing them to keep high familiarity levels. Our novel algorithm is based on the NSGA-II genetic optimization that splits students into well-connected and diverse teams within an organizational network. It optimizes measures of team communication cost and diversity in O(n²) time. The optimization finds Pareto optimal solutions that optimize both metrics, returning teams that have both diversity in member attributes and previous connections between members. We tested the algorithm on real team formation data collected from the MyDreamTeam platform. The solutions provided by the algorithm are superior to the teams assembled by the students, in both diversity and communication cost measures.

Original language	English (US)
Title of host publication	Complex Networks and Their Applications IX - Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020
Editors	Rosa M. Benito, Chantal Cherifi, Hocine Cherifi, Esteban Moro, Luis Mateus Rocha, Marta Sales-Pardo
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	346-357
Number of pages	12
ISBN (Print)	9783030653507
DOIs	https://doi.org/10.1007/978-3-030-65351-4_28
State	Published - 2021
Event	9th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2020 - Madrid, Spain Duration: Dec 1 2020 → Dec 3 2020

Publication series

Name	Studies in Computational Intelligence
Volume	944
ISSN (Print)	1860-949X
ISSN (Electronic)	1860-9503

Conference

Conference	9th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2020
Country/Territory	Spain
City	Madrid
Period	12/1/20 → 12/3/20

Keywords

Diversity
Genetic algorithms
Social networks
Team formation

ASJC Scopus subject areas

Artificial Intelligence

Access to Document

10.1007/978-3-030-65351-4_28

Cite this

Das, A., Gómez-Zará, D., & Contractor, N. (2021). Forming Diverse Teams Based on Members’ Social Networks: A Genetic Algorithm Approach. In R. M. Benito, C. Cherifi, H. Cherifi, E. Moro, L. M. Rocha, & M. Sales-Pardo (Eds.), Complex Networks and Their Applications IX - Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020 (pp. 346-357). (Studies in Computational Intelligence; Vol. 944). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-65351-4_28

Das, Archan ; Gómez-Zará, Diego ; Contractor, Noshir. / Forming Diverse Teams Based on Members’ Social Networks : A Genetic Algorithm Approach. Complex Networks and Their Applications IX - Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020. editor / Rosa M. Benito ; Chantal Cherifi ; Hocine Cherifi ; Esteban Moro ; Luis Mateus Rocha ; Marta Sales-Pardo. Springer Science and Business Media Deutschland GmbH, 2021. pp. 346-357 (Studies in Computational Intelligence).

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title = "Forming Diverse Teams Based on Members{\textquoteright} Social Networks: A Genetic Algorithm Approach",

abstract = "Previous research shows that diverse teams in background and skills can outperform homogeneous teams. However, people often prefer to work with others who are similar and familiar and fail to assemble teams with high diversity levels. We propose a team formation algorithm that suggests diverse teams based on individuals{\textquoteright} social networks, allowing them to keep high familiarity levels. Our novel algorithm is based on the NSGA-II genetic optimization that splits students into well-connected and diverse teams within an organizational network. It optimizes measures of team communication cost and diversity in O(n2) time. The optimization finds Pareto optimal solutions that optimize both metrics, returning teams that have both diversity in member attributes and previous connections between members. We tested the algorithm on real team formation data collected from the MyDreamTeam platform. The solutions provided by the algorithm are superior to the teams assembled by the students, in both diversity and communication cost measures.",

keywords = "Diversity, Genetic algorithms, Social networks, Team formation",

author = "Archan Das and Diego G{\'o}mez-Zar{\'a} and Noshir Contractor",

note = "Publisher Copyright: {\textcopyright} 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 9th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2020 ; Conference date: 01-12-2020 Through 03-12-2020",

year = "2021",

doi = "10.1007/978-3-030-65351-4_28",

language = "English (US)",

isbn = "9783030653507",

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editor = "Benito, {Rosa M.} and Chantal Cherifi and Hocine Cherifi and Esteban Moro and Rocha, {Luis Mateus} and Marta Sales-Pardo",

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address = "Germany",

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Das, A, Gómez-Zará, D & Contractor, N 2021, Forming Diverse Teams Based on Members’ Social Networks: A Genetic Algorithm Approach. in RM Benito, C Cherifi, H Cherifi, E Moro, LM Rocha & M Sales-Pardo (eds), Complex Networks and Their Applications IX - Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020. Studies in Computational Intelligence, vol. 944, Springer Science and Business Media Deutschland GmbH, pp. 346-357, 9th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2020, Madrid, Spain, 12/1/20. https://doi.org/10.1007/978-3-030-65351-4_28

Forming Diverse Teams Based on Members’ Social Networks: A Genetic Algorithm Approach. / Das, Archan; Gómez-Zará, Diego; Contractor, Noshir.
Complex Networks and Their Applications IX - Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020. ed. / Rosa M. Benito; Chantal Cherifi; Hocine Cherifi; Esteban Moro; Luis Mateus Rocha; Marta Sales-Pardo. Springer Science and Business Media Deutschland GmbH, 2021. p. 346-357 (Studies in Computational Intelligence; Vol. 944).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Forming Diverse Teams Based on Members’ Social Networks

T2 - 9th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2020

AU - Das, Archan

AU - Gómez-Zará, Diego

AU - Contractor, Noshir

PY - 2021

Y1 - 2021

N2 - Previous research shows that diverse teams in background and skills can outperform homogeneous teams. However, people often prefer to work with others who are similar and familiar and fail to assemble teams with high diversity levels. We propose a team formation algorithm that suggests diverse teams based on individuals’ social networks, allowing them to keep high familiarity levels. Our novel algorithm is based on the NSGA-II genetic optimization that splits students into well-connected and diverse teams within an organizational network. It optimizes measures of team communication cost and diversity in O(n2) time. The optimization finds Pareto optimal solutions that optimize both metrics, returning teams that have both diversity in member attributes and previous connections between members. We tested the algorithm on real team formation data collected from the MyDreamTeam platform. The solutions provided by the algorithm are superior to the teams assembled by the students, in both diversity and communication cost measures.

AB - Previous research shows that diverse teams in background and skills can outperform homogeneous teams. However, people often prefer to work with others who are similar and familiar and fail to assemble teams with high diversity levels. We propose a team formation algorithm that suggests diverse teams based on individuals’ social networks, allowing them to keep high familiarity levels. Our novel algorithm is based on the NSGA-II genetic optimization that splits students into well-connected and diverse teams within an organizational network. It optimizes measures of team communication cost and diversity in O(n2) time. The optimization finds Pareto optimal solutions that optimize both metrics, returning teams that have both diversity in member attributes and previous connections between members. We tested the algorithm on real team formation data collected from the MyDreamTeam platform. The solutions provided by the algorithm are superior to the teams assembled by the students, in both diversity and communication cost measures.

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KW - Social networks

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BT - Complex Networks and Their Applications IX - Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020

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A2 - Cherifi, Chantal

A2 - Cherifi, Hocine

A2 - Moro, Esteban

A2 - Rocha, Luis Mateus

A2 - Sales-Pardo, Marta

PB - Springer Science and Business Media Deutschland GmbH

Y2 - 1 December 2020 through 3 December 2020

ER -

Das A, Gómez-Zará D, Contractor N. Forming Diverse Teams Based on Members’ Social Networks: A Genetic Algorithm Approach. In Benito RM, Cherifi C, Cherifi H, Moro E, Rocha LM, Sales-Pardo M, editors, Complex Networks and Their Applications IX - Volume 2, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020. Springer Science and Business Media Deutschland GmbH. 2021. p. 346-357. (Studies in Computational Intelligence). doi: 10.1007/978-3-030-65351-4_28

Forming Diverse Teams Based on Members’ Social Networks: A Genetic Algorithm Approach

Abstract

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Keywords

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