My overall research interest is focused on investigating collective behavior of
systems that in many cases, in principle, can be described by simple
local interactions that in all sums up to a network.

In the case of non-trivial network structures, they are often described as
complex networks, and consist of both randomness and structure.
Different structures might arise as a direct consequence of rationales used by the entities
forming a network, or from more indirect reasons by suppressing non
successful structures in terms of functionality of the system described
by the network.

The structure of a network can be analyzed and described by using
various measures. Different structures might produce different dynamics for
systems "living" on top of a network, like the spread of a disease in a social network.
The network in its turn might alter the structure directly or indirectly as
a consequence of the disease and one can study the interplay between
structure and dynamics.

Networks can also be used as tools for solving specific tasks, like
finding communities of correlated stocks in the stock-market,
properties of the configuration space of complex systems, etc...

Another interest is quantitative linguistics and word frequencies. This field is quite similar
to that of networks due to the similarities in the mathematical approach and modeling.
Instead of a node we have a word, and instead of the number of connections we have the frequency
of which it is used in a text. I have been mainly interested in how certain statistical properties
depend on the length of the text (size of the system).

I have also been working on problems related to game theory which addresses questions like what
are the equilibrium strategies for a specific game where no one can enhance their payoff by individually
changing their strategy. The problems I have been working on concerns traffic flow and reversed auction.

In recent years complex networks have drawn a great deal of attention from the physics
community.The recent progress and attention is to a large extent because of all kinds of data of real-world networks found in economy, ecology, biology, etc...

The nature of the networks, showing non-trivial structures -
like scale free distributions, and its applications, makes
them interesting for people with a background in statistical
physics. Many methods used in statistical physics, such as the concept of entropy and Monte-Carlo
simulations, are used to analyze the networks.