Structure inference in complex networks

Zachary club original

Stabilizer analysis

Networks are useful representations of the interactions among the components of complex systems. Still if we want to gain further insights concerning organization principles or global symmetries of the system at hand, it is important to count with tools able to extract information given a graph. Some examples are the so-called communities, groups of nodes with a higher tendency to interact between themselves than with the rest of the network forming relatively isolated clusters, and all the different clustering techniques proposed to find them. Also in some other occasions, a ranking of the nodes can be objectively established as for instance in socioeconomic systems with the wealth, or in transport networks with the nodes capacities. Studying whether and to what extend the elites (based on the ranking variable) control the system resources is an important issue that has gone under the name of rich-club phenomenon in our area. Developing tools to measure this type of phenomena in a critical way, extracting information out of the networks about the mechanisms that led to their formation and time evolution, has become one of my research objectives.

My work in this field has been guided by the following principles

Some recent publications (see the publication list for pdfs):