Tractable frameworks for complex network modelling

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Many modern applications generate large-scale data with a network structure which often includes a temporal component, different modes of connection, and information at edge, node and subgraph levels. Finding tractable statistical frameworks for exploiting such data could have important societal benefits, for example, in cyber-security applications (e.g. intrusion detection, 'fake news'), medicine (e.g. biological networks, genetics), 'artificial intelligence' applications (e.g. recommender systems, sentiment analysis, natural language processing) and more. This PhD will seek to develop such solutions, initially focussing on the approach of embedding, whereby a complex discrete data structure is first transformed into a point cloud, allowing subsequent analysis by more standard statistical and machine-learning techniques such as clustering.