Giulia Preti

PhD Student @ University of Trento

«That quite definitely is the answer. I think the problem, to be quite honest with you, is that you’ve never actually known what the question is.»
“The Hitchhiker's Guide to the Galaxy” by Douglas Adams

All hail!

I am Giulia Preti, a PhD student in Information and Communication Technology at the University of Trento (Italy) under the supervision of Prof. Yannis Velegrakis.

I am a member of the DbTrento research group, working on techniques for mining relevant patterns in multi-weight graphs.

I am also the teaching assistant for the Computability and Computational Complexity course since 2015.

I got my master's degree in Computer Science and my bachelor's degree in mathematics, both of which pursued at the University of Trento.

My research focuses on graphs, a versatile data structure that is increasingly used to model a large plethora of data, and in particular on multi-weighted graphs, i.e., graphs with multiple weights associated to its elements.

My goal is to mine structures in the graph that are relevant with respect to these weights, in an efficient and effective manner.

Publications:

Beyond Frequencies: Graph Pattern Mining in Multi-weighted Graphs.


Proceedings of the 21st International Conference on Extending Database Technology (EDBT), March 26-29, 2018 (PDF) (PPT)

Projects and Code:

ReSuM

Overview

ReSuM is a framework to mine relevant patterns from large weighted and multi-weighted graphs. Assuming that the importance of a pattern is determined not only by its frequency in the graph, but also by the edge weights of its appearances, we propose four scoring functions to compute the relevance of the patterns. These functions satisfy the apriori property, and thus can rely on efficient pruning strategies.

The framework includes an exact and an approximate mining algorithm. The first is characterized by intelligent storage and computation of the pattern scores, while the second is based on the aggregation of similar weighting functions to allow scalability and avoid redundant computations.

Code

The code of this project is publicly available on GitHub, while the datasets used in the experiments may be provided upon request.