Vincenzo (KatolaZ) Nicosia

Lecturer in Networks and Data Analysis

Lecturer in Networks and Data Analysis

*"Science may set limits to knowledge, but should not set limits to imagination"*

(Bertrand Russell)

Research interests

Since I was a child, I have had a genuine curiosity about anything
that exhibits patterns, regularities, self-organisation,
non-trivial interconnections and an overall unexpected
structure. This interest is probably the reason that led me to
start an academic career, driving my research towards the study of
complex systems. In particular, my current research interests
include:

- Structure of complex networks
- Processes on complex topologies
- Time-varying graphs
- Multiplex networks

**socially-inspired peer-to-peer networks**and in the design of control systems for**autonomous mobile robots**.
Structure of complex networks

A large number of experimental evidences have confirmed that many
real systems are can be represented as sets of elementary units
interacting through non-trivial networks of relationships. In the
last fifteen years the theory of complex networks has provided a
comprehensive framework to model and study these systems. The
systematic analysis of the structural properties of a complex
network can reveal important information about the overall
organisation of a system and about the role and function of each of
its elementary units. One of my major contributions in this field is
the extension of the **modularity**function for graphs with**overlapping communities**. I have worked on the**controllability of centrality**in complex networks, on the analysis of**functional human brain networks**, on the problem of defining**three-body correlations**and on the characterisation of the**evolution of road networks**. I am also interested in the analysis of the**evolution of neural networks**and in the characterisation of**symmetries in complex networks**.
Processes on complex topologies

The diffusion of a rumour on Facebook, the spread of a disease in a
country and of a virus in a corporate computer network, the
anomalous synchronisation of large areas of the brain observed
during an epileptic seizure, the formation of consensus and the
evolution of trends and tendencies on Twitter are all examples of
processes which evolve over a complex network. I have worked on the
characterisation of different processes occurring on complex
topologies, including **biased random walks**,**evolution of competing species**,**synchronisation**of mobile agents,**spreading of diseases**and diffusion of information. In particular, I am interested in the characterisation of**first passage times**for different classes of independent random walks on networks and in quantifying the impact of network structure on the evolution of**interacting random walks**. Time-varying graphs

Real networked systems, e.g. online social networks, contact
networks, functional networks of areas in the human brain, are
inherently dynamic, since the relationships among the nodes are not
persistent and usually fluctuate over time. However, up until
recently the studies on complex networks have been entirely based on
static graphs, where the connections among the nodes are given once
and for all. In the last few years, the concept of **time-varying graph**has been proposed as a model to incorporate time in the description of complex networks. I have worked on the**extension of centrality metrics**(including closeness and betweenness) and on the definition of**connectedness and connected components**for time-varying graphs, and I am currently working on the problem of defining and detecting**temporal communities**. I am also interested in the development of**models of temporal graphs**, and on the study of dynamical**processes on time-varying graphs**, including disease spreading, synchronisation and cooperative games. Multiplex networks

Networks are not isolated objects. The same set of units
might be connected through a variety of different interactions, and one
node can be part of several systems at the same time, so that very often
networks are intertwined, interact and co-evolve with other networks. This
is for instance the case of social systems, where the same group of people
might be connected at the same time through friendship, professional
collaboration, online communication, face-to-face interaction, etc. Or of
intermodal transportation systems, in which a set of locations is usually
connected by bus, tranin, underground, aerial and naval transportation links.
All these systems can be view as **multi-layer**or**multiplex networks**, in which the different relationships among the nodes of the system are represented as separate, yet interacting layers. I believe that many complex systems from the real-world can be indeed casted in the recently proposed multiplex framework, and that the investigation of the structure and dynamics of multiplex networks might pave the way towards a better understanding of complex systems. I am interested in**generative models**for multiplex networks, in**structural measures**for the characterisation of multi-layer systems and in**dynamical processes**occurring on multiplex topologies, including**random walks**,**synchronisation**,**information spreading**and**opinion formation**.