To address research questions across the sciences, we develop and apply creative and effective mathematics, algorithms, and visualizations in integrated methods and tools that we make easy to use for other researchers and students.
In an intuitive way, network science can efficiently solve problems in many disciplines that work with complex relational data. A grand challenge in network science is how to best simplify and highlight essential regularities in networks into maps.
Mapping networks is a holy grail of data science because in the myriad links and nodes of a network hide answers to how we can predict how the network will evolve. Based on information theory, we have taken a novel approach to mapping networks. But coming up with the underlying math is not enough.
Mapping large networks requires efficient algorithms as well. And comprehending and communicating the results requires compelling visualizations such as our alluvial diagrams for mapping change over time. When the right math, algorithms, and visualizations come together and work in synergy, we can make exciting discoveries in complex relational data.
We enjoy collaborating with researchers in exciting projects and want to help you analyze and comprehend your rich relational data. Please check out our tools for automating the process of going from raw data to insightful maps and discoveries on mapequation.org.
We are as passionate about the tools as we are about the problems we can tackle with these tools.