May 25, 2018 –
Presenter: Silvia Giordano
Location: Maxwell conference room
As the world grows, all its elements become more and more complex, but the world becomes more globally connected, and its elements more interconnected and interdependent. Complex Networks Theory provides tools and frameworks to dive into the patterns and dynamics of connectivity underlying behaviors of such complexity, by understanding interconnections and interdependencies.
Many of such complex systems are characterized by a large amount of data and the more powerful algorithms to deal with them are the Machine Learning ones. However, Machine Learning solutions act primarily as black boxes and do not give room of maneuver.
We examine the benefits of combining Machine Learning and Complex Networks for potential tradeoff, and we give some quantitative measure with the example of some specific study-cases.