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Title: Edge prediction and community detection in complex networks
Author: Yan , Bowen
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2013
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Many complex systems can be represented as networks, with vertices for individuals and edges denoting relations between them. The study of the structure and properties of a network can help to understand the behaviour of elements in the network in order to improve the productivity and quality of life of humans. This thesis aims at exploring the structure of complex networks and the impact of the structure on their behaviour. It is motivated by two problems in network analysis: community detection and edge prediction. In this thesis, we develop a series of techniques for predicting missing edges and detecting communities in complex networks. One of the significant findings is that some existing techniques in these two areas can be used in complementary ways. For example, missing edges are more likely to be found within communities than between different communities, and the community structure can be discovered by extra information on edges, for example, weights, by using the feature of vertex similarity. We also analysed the influence of different types of missing edges on network analysis methods. Another hypothesis is that a community can be defined as a clique with missing edges, inspired a new community detection algorithm. Finally, we extended a popular sampling method in epidemiology to allow the recovery of the structure, especially the community structure, of a network from samples. Another interesting finding is that we can even use key vertices found from samples to control the spread of an infection in the original network.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available