Inferring social network structure in ecological systems from spatio-temporal data streams.
We propose a methodology for extracting social network structure from spatio-temporal datasets that describe timestamped occurrences of individuals. Our approach identifies temporal regions of dense agent activity and links are drawn between individuals based on their co-occurrences across these 'gathering events'. The statistical significance of these connections is then tested against an appropriate null model. Such a framework allows us to exploit the wealth of analytical and computational tools of network analysis in settings where the underlying connectivity pattern between interacting agents (commonly termed the adjacency matrix) is not given a priori. We perform experiments on two large-scale datasets (greater than 10(6) points) of great tit Parus major wild bird foraging records and illustrate the use of this approach by examining the temporal dynamics of pairing behaviour, a process that was previously very hard to observe. We show that established pair bonds are maintained continuously, whereas new pair bonds form at variable times before breeding, but are characterized by a rapid development of network proximity. The method proposed here is general, and can be applied to any system with information about the temporal co-occurrence of interacting agents.
Pattern Analysis and Machine Learning Research Group, University of Oxford, Oxford, UK. ioannis.psorakis@eng.ox.ac.uk
Ioannis
I
Psorakis
Stephen J
SJ
Roberts
Iead
I
Rezek
Ben C
BC
Sheldon
D000818:Animals; D017753:Ecosystem; D004739:England; D008962:Models, Theoretical; D010152:Pair Bond; D046109:Passeriformes / Q000502:physiology*; D012919:Social Behavior; D013037:Spatial Behavior / Q000502:physiology*; D013997:Time Factors