Community structure in animal social networks
In this thesis the ideas of network analysis are applied to systems of group living
animals. A method of constructing a network of associations by combining
group memberships is presented. Methods of filtering the network according to
association strength are discussed.
The detection and understanding of community structure within animal social
networks forms an important part of this thesis. By allowing the researcher to
study (and verify the statistical significance of) intermediate scale structure in the
network an insight into the biological processes which may motivate the structure
can be obtained.
The various methods which have been proposed to detect community structure in
networks are reviewed. The use of simulated annealing to detect the structure is
discussed. This technique offers the greatest sensitivity in detecting communities,
making it very suitable for the detection of the subtle structures that may exist
in the constructed network.
Two case studies of group living animals are considered: a population of wild
guppies and a population of Galapagos Sea lions. In both systems statistically
significant community structure is found. The biological processes underlying the
observed structure are discussed.
In the latter part of this thesis some methods of constructing model networks with
realistic community structure are discussed. Inspired by the biological aspects of
the earlier part of the thesis; these offer methods of building networks in which the
size, strength, and number of communities can be controlled by the researcher.