Giulia Preti, PhD

Research Interests

My research focuses on graphs, a versatile data model that has been increasingly used to represent a large plethora of data, from biology to social networks, and from computer networks to smart cities. In particular, I consider weighted graphs and dynamic graphs.

Weighted graphs are graphs whose nodes and edges are labeled with weights indicating their relevance or quality. Moreover, in applications aiming at offering personalized products and services to each individual user rather than ``one size fits all'' solutions, each element of the graph naturally carries multiple weights, one for each user.
My goal is to identify structures that appear frequently in the graph and whose appearances are characterized by large weights, and hence are relevant for the user, under the assumption that larger weights indicate higher interest.

Dynamic graphs are graphs that change over time, meaning that their nodes and edges can undergo both structural and attribute changes. They are generally modeled as sequences of static graphs called snapshots. In this context, I am interested in detecting groups of edges that evolve in a convergent manner, meaning that they display a positive correlation on their behavior. These groups of correlated edges, especially when they involve edges that are topologically close, can represent regions of interest in the network.

During my PhD studies, I also worked on entity resolution in highly heterogeneous and temporal databases, defined as collections of records characterized by different schemata and timestamps indicating the date of creation. The reconciliation of the records in this kind of situation, requires specialized similarity functions that take into consideration both the heterogeneity and the dynamism of the data. In my work, I proposed a suitable time-aware schema-agnostic similarity measure and a framework that uses this measure to identify maximal groups of similar temporal records.