Graphs are all around us, but how much do you think about the individual data points and how they connect?
Dr. Sophie Spirkl investigates “structural graph theory,” an area of mathematics and theoretical computer science concerned with large-scale complex networks. She works with graphs, which are models for all kinds of networks. For example, it could be physical roads, hyperlinks or social networks. These huge networks are far from random.
“Take a recipe blog. It is much more likely to link to another recipe than to one of my papers,” says Spirkl, Associate Professor in Combinatorics and Optimization. “But what are the ramifications of this kind of localized non-randomness when we look at the big picture? That's what I like to think about.”
Imagine a graph containing all websites that exist, connected by hyperlinks between them. This is much too large a network to visualize, but – based on the above local observation – we can predict that the global structure would form clusters based on topics: recipes usually link to other recipes; math websites usually link to even more math. Based on this, if you wanted to reach a large audience, it would be most effective to spread a message (such as an advertisement) across a range of topics. Spirkl's research formalizes these ideas in an abstract setting, leading to structural insights and algorithmic innovations with a broad range of applications.
The latest round of the Canada Research Chairs (CRC) Program recognizes Spirkl’s work in structural graph theory naming her a Tier 2 Canada Research Chair in Graph Structure and Algorithms.
The Program helps Canadian universities attract and retain world-class researchers. It also supports scholars whose discoveries strengthen Canada’s research capacity while helping to train the next generation of innovators and leaders.
Spirkl says, “I feel honoured, of course, but beyond that I'm honestly just very excited for the opportunities that this award is going to provide for my students.”