Skip to main content

Inferring social structure from temporal data

Abstract

Social network analysis has become a popular tool for characterising the social structure of populations. Animal social networks can be built either by observing individuals and defining links based on the occurrence of specific types of social interactions, or by linking individuals based on observations of physical proximity or group membership, given a certain behavioural activity. The latter approaches of discovering network structure require splitting the temporal observation stream into discrete events given an appropriate time resolution parameter. This process poses several non-trivial problems which have not received adequate attention so far. Here, using data from a study of passive integrated transponder (PIT)-tagged great tits Parus major, we discuss these problems, demonstrate how the choice of the extraction method and the temporal resolution parameter influence the appearance and properties of the retrieved network and suggest a modus operandi that minimises observer bias due to arbitrary parameter choice. Our results have important implications for all studies of social networks where associations are based on spatio-temporal proximity, and more generally for all studies where we seek to uncover the relationships amongst a population of individuals that are observed through a temporal data stream of appearance records.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  • Aplin LM, Farine DR, Morand-Feron J, Cole EF, Cockburn A, Sheldon BC (2013) Individual personalities predict social behaviour in wild networks of great tits (Parus major). Ecol Lett 16:1365–1372

    Article  CAS  PubMed  Google Scholar 

  • Barthelemy M, Barrat A, Pastor-Satorras R, Vespignani A (2005) Characterization and modeling of weighted networks. Physica A 346:34–43

    Article  Google Scholar 

  • Beijder L, Fletcher D, Brager S (1998) A method for testing association patterns of social animals. Anim Behav 56:719–725

    Article  Google Scholar 

  • Cairns SJ, Schwager SJ (1987) A comparison of association indices. Anim Behav 35:1454–1469

    Article  Google Scholar 

  • Croft DP, James R, Krause J (2008) Exploring animal social networks. Princeton University Press, Princeton

    Book  Google Scholar 

  • Danon L, Diaz-Guilera A, Duch J, Arenas A (2005) Comparing community structure identification. J Stat Mech-Theory Exp 09, P09008

    Google Scholar 

  • Farine DR, Garroway CJ, Sheldon BC (2012) Social network analysis of mixed-species flocks: exploring the structure and evolution of interspecific social behaviour. Anim Behav 84:1271–1277

    Article  Google Scholar 

  • Franks DW, Ruxton GD, James R (2010) Sampling animal association networks with the gambit of the group. Behav Ecol Sociobiol 64:493–503

    Article  Google Scholar 

  • Freeman LC (1979) Centrality in social networks. I: conceptual clarification. Soc Networks 1:215–239

    Article  Google Scholar 

  • Garroway CJ, Radersma R, Hinde CA (2015) Perspectives on social network analyses of bird populations. In: Krause J, Croft DP, James R (eds) Animal social networks: perspectives and challenges. Oxford University Press, Oxford, pp 171–183

    Google Scholar 

  • Gibbons JW, Andrews KM (2004) PIT tagging: simple technology at its best. Bioscience 54:447–454

    Article  Google Scholar 

  • Ginsberg JR, Young TP (1992) Measuring associations between individuals or groups in behavioural studies. Anim Behav 44:377–379

    Article  Google Scholar 

  • Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99:7821–7826

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  • Gossler A (1993) The great tit. Hamlin, London

    Google Scholar 

  • Hinde R (1952) The behaviour of the great tit (Parus major) and some other related species. Behav Suppl 2:1–201

    Google Scholar 

  • James R, Croft DP, Krause J (2009) Potential banana skins in animal social network analysis. Behav Ecol Sociobiol 63:989–997

    Article  Google Scholar 

  • Krause J, Krause S, Arlinghaus R, Psorakis I, Roberts S, Rutz C (2013) Reality mining of animal social systems. Trends Ecol Evol 28:541–551

    Article  PubMed  Google Scholar 

  • Lahti K, Koivula K, Orell M (1997) Dominance, daily activity and winter survival in willow tits: detrimental cost of long working hours? Behaviour 134:921–939

    Article  Google Scholar 

  • Newman MEJ (2010) Networks: an introduction. Oxford University Press, Oxford

    Book  Google Scholar 

  • Perrins CM (1979) British tits. Collins, London

    Google Scholar 

  • Psorakis I, Roberts SJ, Rezek I, Sheldon BC (2012) Inferring social network structure in ecological systems from spatio-temporal data streams. J R Soc Interface 9:3055–3066

    Article  PubMed Central  PubMed  Google Scholar 

  • Rutz C, Burns ZT, James R, Ismar SMH, Burt J, Otis B, Bowen J, St Clair JJH (2012) Automated mapping of social networks in wild birds. Curr Biol 22:R669–R671

    Article  CAS  PubMed  Google Scholar 

  • Saitou T (1978) Ecological study of social organization in the great tit Parus major L. I. Basic structure of winter flock. Jpn J Ecol 28:199–214

    Google Scholar 

  • Smyth B, Nebel S (2013) Passive integrated transponder (PIT) tags in the study of animal movement. Nat Educ Knowl 4:3

    Google Scholar 

  • Wasserman S, Faust K (1994) Social network analysis: methods and applications. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Watts DJ (1999) Small worlds: the dynamics of networks between order and randomness. Princeton University Press, Princeton

    Google Scholar 

  • Whitehead H (2008) Analyzing animal societies. The University of Chicago Press, Chicago

    Book  Google Scholar 

  • Whitehead H, Dufault S (1999) Techniques for analyzing vertebrate social structure using identified individuals: review and recommendations. Adv Stud Behav 28:33–74

    Article  Google Scholar 

  • Wilson EO (1975) Sociobiology: the new synthesis. Harvard University Press, Cambridge

    Google Scholar 

Download references

Acknowledgements

We thank J. Howe, T. Wilkin, S. Evans, A. Hinks and A. Grabowska for assistance in the field and three anonymous reviewers for valuable comments on the manuscript. This research was funded by an ERC grant to BCS (AdG 250164) and a Microsoft Research Grant to IP. LMA was also funded by an Australian Postgraduate Award and by an International Alliance of Research Universities travel grant.

Ethical standards

All authors declare that the present study complies with the current laws in the UK.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bernhard Voelkl.

Additional information

Communicated by D. P. Croft

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(PDF 566 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Psorakis, I., Voelkl, B., Garroway, C.J. et al. Inferring social structure from temporal data. Behav Ecol Sociobiol 69, 857–866 (2015). https://doi.org/10.1007/s00265-015-1906-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00265-015-1906-0

Keywords

  • Social networks
  • Group detection
  • Flocks
  • Gathering events
  • Great tits