Modelling and Mining Complex Networks

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In the big data era, data is considered as the new fossil fuel. Every human-technology interaction, or sensor network, generates new data points that can be viewed, based on the type of interaction, as a self-organizing network. In these networks (for example, Facebook) nodes not only contain some useful information (such as user's profile, photos, tags) but are also internally connected to other nodes (relations based on friendship, similar users behaviour, age, geographic location). Such networks are large-scale, self-organizing, decentralized, and evolve dynamically over time. As a result, random geometric graphs turn out to be natural and well suited in modelling them. Understanding the principles driving the organization and behaviour of complex networks, as well as algorithms based on these networks, is crucial for a broad range of fields, including information and social sciences, economics, biology, and neuroscience.******The research program of the PI contributes to our understanding by focusing on modelling and mining of complex networks (pure research as well as applied one with industry partners). The program is multidisciplinary in nature and as such requires a rather unique blend of knowledge, skills, and tools from at least three areas: 1) mathematics and theoretical computer science, 2) social science, and 3) applied computer science. Increasingly, all three areas are interconnected, and experience in all of them is required in order to make an important impact in the field.