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Title: Probabilistic inference in ecological networks : graph discovery, community detection and modelling dynamic sociality
Author: Psorakis, Ioannis
ISNI:       0000 0004 2747 6201
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2013
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This thesis proposes a collection of analytical and computational methods for inferring an underlying social structure of a given population, observed only via timestamped occurrences of its members across a range of locations. It shows that such data streams have a modular and temporally-focused structure, neither fully ordered nor completely random, with individuals appearing in "gathering events". By exploiting such structure, the thesis proposes an appropriate mapping of those spatio-temporal data streams to a social network, based on the co-occurrences of agents across gathering events, while capturing the uncertainty over social ties via the use of probability distributions. Given the extracted graphs mentioned above, an approach is proposed for studying their community organisation. The method considers communities as explanatory variables for the observed interactions, producing overlapping partitions and node membership scores to groups. The aforementioned models are motivated by a large ongoing experiment at Wytham woods, Oxford, where a population of Parus major wild birds is tagged with RFID devices and a grid of feeding locations generates thousands of spatio-temporal records each year. The methods proposed are applied on such data set to demonstrate how they can be used to explore wild bird sociality, reveal its internal organisation across a variety of different scales and provide insights into important biological processes relating to mating pair formation.
Supervisor: Roberts, Stephen; Sheldon, Ben Sponsor: Microsoft Research
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Probability theory and stochastic processes ; Statistics (see also social sciences) ; Probability ; Pattern recognition (statistics) ; Information engineering ; Zoological sciences ; Behaviour (zoology) ; Ecology (zoology) ; network analysis ; community detection ; graph mining ; ecological networks ; graph theory