Inferring social structure from temporal data
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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.
KeywordsSocial networks Group detection Flocks Gathering events Great tits
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.
All authors declare that the present study complies with the current laws in the UK.
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