research-article

The Impact of Centrality on Individual and Collective Performance in Social Problem-Solving Systems

Authors:

Diego Vrague Noble,

Ricardo Matsumura Araújo,

Luis da Cunha LambAuthors Info & Claims

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

Pages 73 - 80

https://doi.org/10.1145/2739480.2754679

Published: 11 July 2015 Publication History

Abstract

In this paper, we analyze the dependency between centrality and individual performance in socially-inspired problem-solving systems. By means of extensive numerical simulations, we investigate how individual performance in four different models correlate with four different classical centrality measures. Our main result shows that there is a high linear correlation between centrality and individual performance when individuals systematically exploit central positions. In this case, central individuals tend to deviate from the expected majority contribution behavior. Although there is ample evidence about the relevance of centrality in social problem-solving, our work contributes to understand that some measures correlate better with individual performance than others due to individual traits, a position that is gaining strength in recent studies.

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Cited By

Grando FGranville LLamb L(2018)Machine Learning in Network Centrality MeasuresACM Computing Surveys10.1145/323719251:5(1-32)Online publication date: 22-Oct-2018
https://dl.acm.org/doi/10.1145/3237192
Grando FLamb L(2018)Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model2018 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2018.8489690(1-8)Online publication date: Jul-2018
https://doi.org/10.1109/IJCNN.2018.8489690
Grando FLamb L(2016)On approximating networks centrality measures via neural learning algorithms2016 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2016.7727248(551-557)Online publication date: Jul-2016
https://doi.org/10.1109/IJCNN.2016.7727248
Show More Cited By

Index Terms

The Impact of Centrality on Individual and Collective Performance in Social Problem-Solving Systems
1. Computing methodologies
  1. Artificial intelligence
    1. Distributed artificial intelligence
      1. Multi-agent systems
  2. Modeling and simulation
    1. Simulation theory
      1. Network science
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing theory, concepts and paradigms
      1. Social networks

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Published In

cover image ACM Conferences

GECCO '15: Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation

July 2015

1496 pages

ISBN:9781450334723

DOI:10.1145/2739480

Editor:
Sara Silva
Universidade de Lisboa, Portugal
,
General Chair:
Anna I. Esparcia-Alcázar
Universitat Politècnica de València, Spain

Copyright © 2015 ACM.

© 2015 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2015

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This work is partly supported by the Brazilian Research Council CNPq and the CAPES Foundation.

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GECCO '15

Sponsor:

SIGEVO

GECCO '15: Genetic and Evolutionary Computation Conference

July 11 - 15, 2015

Madrid, Spain

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GECCO '15 Paper Acceptance Rate 182 of 505 submissions, 36%;

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Grando FGranville LLamb L(2018)Machine Learning in Network Centrality MeasuresACM Computing Surveys10.1145/323719251:5(1-32)Online publication date: 22-Oct-2018
https://dl.acm.org/doi/10.1145/3237192
Grando FLamb L(2018)Computing Vertex Centrality Measures in Massive Real Networks with a Neural Learning Model2018 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2018.8489690(1-8)Online publication date: Jul-2018
https://doi.org/10.1109/IJCNN.2018.8489690
Grando FLamb L(2016)On approximating networks centrality measures via neural learning algorithms2016 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN.2016.7727248(551-557)Online publication date: Jul-2016
https://doi.org/10.1109/IJCNN.2016.7727248
Grando FNoble DLamb L(2016)An Analysis of Centrality Measures for Complex and Social Networks2016 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOCOM.2016.7841580(1-6)Online publication date: Dec-2016
https://doi.org/10.1109/GLOCOM.2016.7841580

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