Literature DB >> 24944435

Modelling the evolution of a bi-partite network Peer referral in interlocking directorates.

Johan Koskinen1, Christofer Edling2.   

Abstract

A central part of relational ties between social actors are constituted by shared affiliations and events. The action of joint participation reinforces personal ties between social actors as well as mutually shared values and norms that in turn perpetuate the patterns of social action that define groups. Therefore the study of bipartite networks is central to social science. Furthermore, the dynamics of these processes suggests that bipartite networks should not be considered static structures but rather be studied over time. In order to model the evolution of bipartite networks empirically we introduce a class of models and a Bayesian inference scheme that extends previous stochastic actor-oriented models for unimodal graphs. Contemporary research on interlocking directorates provides an area of research in which it seems reasonable to apply the model. Specifically, we address the question of how tie formation, i.e. director recruitment, contributes to the structural properties of the interlocking directorate network. For boards of directors on the Stockholm stock exchange we propose that a prolific mechanism in tie formation is that of peer referral. The results indicate that such a mechanism is present, generating multiple interlocks between boards.

Entities:  

Keywords:  Bayesian analysis; Bipartite graphs; Interlocking directorates; Longitudinal network data; Prediction; Stochastic actor-oriented models

Year:  2012        PMID: 24944435      PMCID: PMC4059769          DOI: 10.1016/j.socnet.2010.03.001

Source DB:  PubMed          Journal:  Soc Networks        ISSN: 0378-8733


  2 in total

1.  Random graphs with arbitrary degree distributions and their applications.

Authors:  M E Newman; S H Strogatz; D J Watts
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2001-07-24

2.  MAXIMUM LIKELIHOOD ESTIMATION FOR SOCIAL NETWORK DYNAMICS.

Authors:  Tom A B Snijders; Johan Koskinen; Michael Schweinberger
Journal:  Ann Appl Stat       Date:  2010-06-01       Impact factor: 2.083

  2 in total
  11 in total

1.  Interlocking directorates in Irish companies using a latent space model for bipartite networks.

Authors:  Nial Friel; Riccardo Rastelli; Jason Wyse; Adrian E Raftery
Journal:  Proc Natl Acad Sci U S A       Date:  2016-05-31       Impact factor: 11.205

2.  A model for the multiplex dynamics of two-mode and one-mode networks, with an application to employment preference, friendship, and advice.

Authors:  Tom A B Snijders; Alessandro Lomi; Vanina Jasmine Torló
Journal:  Soc Networks       Date:  2013-05

3.  Analytic Strategies for Longitudinal Networks with Missing Data.

Authors:  Kayla de la Haye; Joshua Embree; Marc Punkay; Dorothy L Espelage; Joan S Tucker; Harold D Green
Journal:  Soc Networks       Date:  2017-03-03

4.  A coevolving model based on preferential triadic closure for social media networks.

Authors:  Menghui Li; Hailin Zou; Shuguang Guan; Xiaofeng Gong; Kun Li; Zengru Di; Choy-Heng Lai
Journal:  Sci Rep       Date:  2013       Impact factor: 4.379

5.  A collaborative recommend algorithm based on bipartite community.

Authors:  Yuchen Fu; Quan Liu; Zhiming Cui
Journal:  ScientificWorldJournal       Date:  2014-04-13

6.  The evolution of research collaboration within and across disciplines in Italian Academia.

Authors:  Elisa Bellotti; Luka Kronegger; Luigi Guadalupi
Journal:  Scientometrics       Date:  2016-07-15       Impact factor: 3.238

7.  Look who's talking: Two-mode networks as representations of a topic model of New Zealand parliamentary speeches.

Authors:  Ben Curran; Kyle Higham; Elisenda Ortiz; Demival Vasques Filho
Journal:  PLoS One       Date:  2018-06-20       Impact factor: 3.240

8.  Latent influence networks in global environmental politics.

Authors:  Benjamin W Campbell; Frank W Marrs; Tobias Böhmelt; Bailey K Fosdick; Skyler J Cranmer
Journal:  PLoS One       Date:  2019-03-07       Impact factor: 3.240

9.  Distribution of Node Characteristics in Evolving Tripartite Network.

Authors:  Ladislav Beranek; Radim Remes
Journal:  Entropy (Basel)       Date:  2020-02-25       Impact factor: 2.524

10.  Backbone: An R package for extracting the backbone of bipartite projections.

Authors:  Rachel Domagalski; Zachary P Neal; Bruce Sagan
Journal:  PLoS One       Date:  2021-01-06       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.