Skip to main content
Book cover

Encyclopedia of Social Network Analysis and Mining pp 9–18Cite as

Actor-Based Models for Longitudinal Networks

  • 216 Accesses

Synonyms

Actor-oriented modelling; Agent-based models; Stochastic actor-based models

Glossary

Actors:

Nodes of the network graph

Behavior:

Changing characteristics of actors

Covariates:

Variables which can depend on the actors (actor covariates) or on pairs of actors (dyadic covariates). They are considered “exogenous” variables in the sense that they are not determined by the stochastic process underlying the model

Dyad:

Pair of actors of the network

Dyadic Indicator:

Binary variable indicating the presence or absence of a tie between two actors

Effects:

Specifications of the objective function

Longitudinal Networks:

Repeated measures of networks over time

Markov Chain:

Stochastic process where the probability of future states given the present state does not depend on past states

Method of Moments:

Statistical estimation method consisting of equating sample moments of a distribution with unobserved theoretic moments in order to get an approximation to the solutions of the likelihood...

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-1-4939-7131-2_166
  • Chapter length: 10 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   2,500.00
Price excludes VAT (USA)
  • ISBN: 978-1-4939-7131-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   4,499.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Actor-Based Models for Longitudinal Networks, Fig. 1
Actor-Based Models for Longitudinal Networks, Fig. 2

References

  • Brass D, Galaskiewicz J, Greve H, Tsai W (2004) Taking stock of networks and organizations: a multilevel perspective. Acad Manag J 47:795–817

    Google Scholar 

  • Burk W, Cr S, Tr S (2007) Beyond dyadic interdependence: actor-oriented models for co-evolving social networks and individual behaviors. Int J Behav Dev 31:397–404

    CrossRef  Google Scholar 

  • Checkley M, Steglich C (2007) Partners in power: job mobility and dynamic deal-making. Eur Manag Rev 4(3):161–171

    CrossRef  Google Scholar 

  • Coleman J (1964) Introduction to mathematical sociology. The Free Press of Glencoe, New York

    Google Scholar 

  • de Nooy W (2002) The dynamics of artistic prestige. Poetics 30:147–167

    CrossRef  Google Scholar 

  • Frank O (1991) Statistical analysis of change in networks. Statistica Neerlandica 45:283–293

    MathSciNet  MATH  CrossRef  Google Scholar 

  • Huisman ME, Steglich CEG (2008) Treatment of non-response in longitudinal network data. Soc Networks 30:297–308

    CrossRef  Google Scholar 

  • Hunter DR, Goodreau SM, Handcock MS (2008) Goodness of fit for social network models. J Am Stat Assoc 103:248–258

    MathSciNet  MATH  CrossRef  Google Scholar 

  • Koskinen JH, Snijders TA (2007) Bayesian inference for dynamic social network data. J Stat Plan Inference 137(12):3930–3938

    MathSciNet  MATH  CrossRef  Google Scholar 

  • Pearson MA, West P (2003) Drifting smoke rings: social network analysis and Markov processes in a longitudinal study of friendship groups and risk-taking. Connections 25(2):59–76

    Google Scholar 

  • Ripley R, Snijders T (2011) Manual for SIENA version 4.0. http://www.stats.ox.ac.uk/siena/

  • Robbins H, Monro S (1951) A stochastic approximation method. Ann Math Stat 22(3):400–407

    MathSciNet  MATH  CrossRef  Google Scholar 

  • Robins GL, Pattison PE, Kalish Y, Lusher D (2007) An introduction to exponential random graph (p*) models for social networks. Soc Networks 29:173–191

    CrossRef  Google Scholar 

  • Sampson SF (1968) A novitiate in a period of change: an experimental and case study of social relationships. PhD thesis, Cornell University

    Google Scholar 

  • Snijders TAB (1996) Stochastic actor-oriented dynamic network analysis. J Math Soc 21:149–172

    MATH  CrossRef  Google Scholar 

  • Snijders TAB (2001) The statistical evaluation of social network dynamics. Sociol Methodol 31(1):361–395

    MathSciNet  CrossRef  Google Scholar 

  • Snijders TAB (2005) Models for longitudinal network data. In: Carrington PJ, Scott J, Wasserman S (eds) Models and methods in social network analysis. Cambridge University Press, Cambridge/New York, pp 215–247

    CrossRef  Google Scholar 

  • Snijders TAB (2006) Statistical methods for network dynamics. In: Luchini SR et al (eds) Proceedings of the XLIII scientific meeting. Italian Statistical Society. CLEUP, Padova, pp 281–296

    Google Scholar 

  • Snijders TAB (2012) Siena in R: RSiena. http://www.stats.ox.ac.uk/snijders/siena/siena_r.htm

  • Snijders TAB, van Duijn MAJ (1997) Simulation for statistical inference in dynamic network models. In: Conte R, Hegselmann R, Terna P (eds) Simulating social phenomena. Springer, Berlin, pp 493–512

    CrossRef  Google Scholar 

  • Snijders TAB, Koskinen J, Schweinberger M (2010) Maximum likelihood estimation for social network dynamics. Ann Appl Stat 4(2):567–588

    MathSciNet  MATH  CrossRef  Google Scholar 

  • van de Bunt GG (1999) Friends by choice; an actor-oriented statistical network model for friendship networks through time. Thesis Publishers, Amsterdam

    Google Scholar 

  • van de Bunt GG, Groenewegen P (2007) An actor-oriented dynamic network approach: the case of interorganizational network evolution. Organ Res Methods 10(3):463–482

    CrossRef  Google Scholar 

  • van Duijn M, Zeggelink EPH, Huisman M, Stokman FN, Wasseur FW (2003) Evolution of sociology freshmen into a friendship network. J Math Sociol 27:153–191

    CrossRef  Google Scholar 

  • Wasserman S (1979) A stochastic model for directed graphs with transition rates determined by reciprocity. In: Schuessler KF (ed) Sociological methodology 1980. Jossey-Bass, San Francisco

    Google Scholar 

Download references

Acknowledgments

Nial Friel’s research was supported by a Science Foundation Ireland Research Frontiers Program grant, 09/RFP/MTH2199.

Author information

Authors and Affiliations

  1. Faculty of Economics, University of Lugano, Lugano, Switzerland

    Alberto Caimo

  2. INSIGHT: The National Centre for Big Data Analytics, School of Mathematical Sciences, University College Dublin, Dublin, Ireland

    Nial Friel

Authors
  1. Alberto Caimo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nial Friel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alberto Caimo .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

  2. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Jon Rokne

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer Science+Business Media LLC, part of Springer Nature

About this entry

Verify currency and authenticity via CrossMark

Cite this entry

Caimo, A., Friel, N. (2018). Actor-Based Models for Longitudinal Networks. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_166

Download citation