Complex networks

Networks are everywhere: social networks, electrical power networks, Bayesian networks, etc. These networks have structure that may not be visible to the naked eye but can be seen through mathematical lenses, analysis techniques that reveal subtle patterns.

We have access to far more data than ever before, and much of this data is not tabular but graphical. Traditional data management and analysis mostly assumes tabular data. New techniques are needed to understand the complex structures of graphical data.

Some questions you may want to ask about a network:

  • Which parts are most influential?
  • What is the best way to measure importance or influence in my particular context?
  • How can I visualize my network or at least some simplification of it?
  • How well does information flow through the network? How could it best be improved?
  • Is there a center to my network, or a small number of centers? How should I define a center?
  • My graph is huge. How do I randomly sample it, preserving structure?
  • Can a simulation produce a network that usefully resembles my actual network?
  • How is my network likely to evolve over time?
  • What sort of communities or clusters are there in my network? How are they bridged?
  • What would happen if a few connections were dropped at random? Or strategically removed by an adversary?

Unlock the insights in your network. Contact us to see how you can get more from your data.


Trusted consultants to some of the world’s leading companies

Amazon, Facebook, Google, US Army Corp of Engineers, Amgen, Microsoft, Hitachi Data Systems