Link Prediction Applied to Tract-tracing Data

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dc.contributor.advisorMorup, M.
dc.contributor.advisorBakker, R.
dc.contributor.advisorGerven, M.A.J. van
dc.contributor.authorKruijswijk, J.M.A.
dc.date.issued2014-08-25
dc.description.abstractTract-tracing studies are invasive and costly, but are still applied since they are more accurate than other techniques that expose the brain's structural connectivity. To reduce the costs of future tract-tracing studies, the present study investigates whether link prediction algorithms, that are normally used for exposing new information in social networks, can be used to maximize the information gained by future tract-tracing studies. Before using a link prediction algorithm on tract-tracing data, the performance is tested using simulated networks that mimic the topological features of human brain networks. The results show that the algorithm performs well on the simulated data and also when applied to tract-tracing data. Various ways to improve the empirical results are discussed.en_US
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/156
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleLink Prediction Applied to Tract-tracing Dataen_US
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