Networks
form the substrate of a dominating class of large-scale
complex systems, encompassing socio-economical relations, the
human brain, cell metabolism, ecosystems, informational
infrastructure, and many more. Being high-dimensional,
heterogeneously structured, and often large relational
objects, networks require special analytical and
methodological frameworks.

I focus on the development of methods to extract scientific
understanding from network data, as well as the mathematical
modeling of network behavior and evolution. I'm particularly
interested in problems of network inference, where meaningful
structural and functional patterns cannot be obtained by
direct inspection or low-order statistics, and require instead
more sophisticated approaches based on large-scale generative
models and efficient algorithms derived from them. In more
demanding scenarios, the network data is noisy, incomplete, or
even completely hidden, leaving its trace only via an observed
dynamical behavior — in which case it needs to be fully
reconstructed from indirect information.

A central aim of my research is to obtain robust methods
and theory that are derived from fundamental principles, while
being at the same time algorithmically efficient and
interpretable. This is achieved by merging analytical tools
and concepts from a variety of disciplines,
including Statistical
Mechanics,Information Theory,
and Bayesian Statistics.

Many of the methods developed in my work are made available
as part of
the graph-tool
library, which
is extensively
documented. For a practical introduction to many inference
and reconstruction algorithms, please refer to
the HOWTO.