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Moreno Bonaventura

 Moreno Bonaventura

Email: m.bonaventura@qmul.ac.uk

email: m.bonaventura@qmul.ac.uk
Tel: +44 (0)20 7882 8570
Location: Mile End, Bancroft Building, Room 4.23

Website: http://www.maths.qmul.ac.uk/~mbonaventura

1st Supervisor: Professor Vito Latora
2nd Supervisor: Dr Pietro Panzarasa

Project Title: 

"Time Varying Complex Networks"

Project Description: 

My research is broadly concerned with the mathematical analysis of the topology and dynamics of time-varying complex networks. 

The representation of a real-world system as a network in which nodes are connected through links is often a powerful abstraction to quantitatively characterise a variety of properties, such as the resilience of a technological system or the social influence played by the key members of an organisation. 

Until recently most network research on complex systems was conducted typically by drawing on cross-sectional datasets that included individuals, groups or organisations observed at the same point in time. However, most real-world networks inevitably vary over time as a result of the addition and removal of nodes, and the creation and severing of ties between nodes. 

A longitudinal approach to network analysis is therefore essential for uncovering the trajectories and mechanisms that are responsible for the emergence of certain collective outcomes from the interaction of a system’s components. For instance, it is only by investigating how the topology of social relationships changes over time that an insight can be gained on the emergence and diffusion of cooperative behaviour in a population of interacting individuals. 

Recently the increasing availability of large-scale electronic datasets has offered new opportunities to empirically analyse real-world networks and their variation over time in a quantitative and rigorous way. My research draws on such datasets to better understand the interplay between the network time-varying topology and the dynamics of processes that occur on the network, such as social influence, cooperative and collective behaviour, and information diffusion. 

My approach to research is problem-driven and interdisciplinary, combining methods from statistical mechanics, graph and probability theory, computational modelling, and the social sciences. Drawing on these methods, my ongoing projects place a special emphasis on sociologically relevant phenomena in a variety of empirical domains, including scientific collaboration, online communication, innovation processes and knowledge creation.

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