Research at TMU:
Complex Networks
“Anyone who’s ever googled anything — they’re benefiting from network science under the hood.”
Most never sense it, but sophisticated mathematics underlies the science and technology that touch our everyday lives. In the digital age, companies are flooded with enormous amounts of data. Valuable, untapped insights lie buried within, but algorithms to unearth them are still scarce. Network science provides solutions.
One third of the faculty in the Department of Mathematics at TMU, known as researchers from Graphs@TMU group, are busy establishing mathematical fundamentals within this field. Among them, Dr. Pawel Pralat has brought the research from purely abstract theory into actual, real world applications — including projects for Canada’s Department of National Defence, The Globe and Mail, Alcatel-Lucent and numerous industry and governmental partners.
“Networks are all around us — social networks are explicit; others are less obvious, as with complex interactions between proteins in the body.”
“We can now use computers to map out and understand these massive networks,” says Pralat, “and even create artificial simulations to test out all kinds of dynamic, interconnected what-if scenarios.”
The results inform critical decision-making in situations that are too costly, risky, or even impossible to experiment with in the real world. Over the years, Pralat has developed the framework to explore problems, including:
- Which subway frequency minimizes the spread of viruses, such as COVID-19, inside large, urban public transportation networks
- How to securely and efficiently transmit sensitive information among smart car networks
- How to design machine learning tools to recommend personalized content to users of online news networks
- Where to place emergency vehicle stations to optimize response time for city dwellers
Pralat’s graph-based tools are internationally recognized and demand outstrips what the lab can supply. He now travels extensively to collaborate on even larger, more sophisticated problems and to share expertise in network science with leading researchers and practitioners around the world.
