Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.626369
Title: Predicting the attributes of nodes in networks
Author: Peel, L.
Awarding Body: University College London (University of London)
Current Institution: University College London (University of London)
Date of Award: 2013
Availability of Full Text:
Access through EThOS:
Full text unavailable from EThOS. Please try the link below.
Access through Institution:
Abstract:
Networks are important. They provide a general framework for representing the relationships or interactions which impose dependencies between entities. Network nodes represent entities and the links between them represent relations/interactions. Nodes' attributes contain non-structural features of the entities. For example, in a social network where nodes represent people and links represent friendships, attributes could represent features such as age, race or gender. In many situations it is easy to observe the link structure of a network, but not the attributes of the nodes. For example, in an on-line social network it may be possible to observe all friendships, but the observability of attributes are determined by the user's privacy settings. As a result the state-of-the-art of learning in networks tend to either focus on clustering nodes with similar link patterns (i.e functional communities) or predicting the missing attribute of nodes (i.e. node labels). In this work we bring these two ideas together to examine how the identification of communities and related structures can be used to predict the hidden attributes of the network nodes. The models we present are effective, flexible and principled. They are effective in their ability to predict discrete (either binary or multivalued) and continuous node attributes in real world network datasets. They are flexible in that they can adapt to and identify a wide range of network structures. They are principled in that they are based on sound theoretical methods of Bayesian statistics. We achieve this by a series of novel extensions to the stochastic blockmodel, a probabilistic generative model for identifying functional communities.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.626369  DOI: Not available
Share: