- What makes networks tick
- Ryerson Mathematician Tops the Charts with the Most NSERC Engage Grants
- Research story: Pawel Pralat advances research into graph theory.
- Research profile video: Pawel Pralat's research is where pure mathematics and real-world issues intersect.
- Faculty of Science Annual Report (2013) and Graduate Studies also feature my research program.
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, the Facebook on-line social network) 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 user's behaviour, age, geographic location). Such networks are large-scale, self-organizing, decentralized, and evolve dynamically over time. 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.
My main research interests lie in graph theory with applications to real-world self-organizing networks such as the web graph or social networks. I am interested in both modelling and searching of complex networks with emphasis on connections to Big Data research questions. Both topics have experienced tremendous growth in the last few years, with an increasing number of applications in other areas of mathematics and computer science.