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Multigroup connectivity structures and their implications

Published online by Cambridge University Press:  18 November 2019

Shadi Mohagheghi Open the ORCID record for Shadi Mohagheghi [Opens in a new window] ,
Pushkarini Agharkar ,
Francesco Bullo  and
Noah E. Friedkin Open the ORCID record for Noah E. Friedkin [Opens in a new window]
Shadi Mohagheghi*
Affiliation:
Department of Electrical and Computer Engineering, University of California at Santa Barbara, Santa Barbara, CA, USA
Pushkarini Agharkar
Affiliation:
Department of Mechanical Engineering, University of California at Santa Barbara, Santa Barbara, CA, USA (emails: [email protected]; [email protected])
Francesco Bullo
Affiliation:
Department of Mechanical Engineering, University of California at Santa Barbara, Santa Barbara, CA, USA (emails: [email protected]; [email protected])
Noah E. Friedkin
Affiliation:
Department of Sociology, University of California at Santa Barbara, Santa Barbara, CA, USA (email: [email protected])
*
*Corresponding author. Email: [email protected]
Rights & Permissions [Opens in a new window]

Abstract

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We investigate the implications of different forms of multigroup connectivity. Four multigroup connectivity modalities are considered: co-memberships, edge bundles, bridges, and liaison hierarchies. We propose generative models to generate these four modalities. Our models are variants of planted partition or stochastic block models conditioned under certain topological constraints. We report findings of a comparative analysis in which we evaluate these structures, controlling for their edge densities and sizes, on mean rates of information propagation, convergence times to consensus, and steady-state deviations from the consensus value in the presence of noise as network size increases.

Keywords

multigroup networksconnectivity modalitiesrandom graphscomparative analysis
Type
Original Article
Information
Network Science , Volume 7 , Issue 4 , December 2019 , pp. 459 - 475
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© Cambridge University Press 2019

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