no code implementations • 16 Feb 2023 • Luca Becchetti, Andrea Clementi, Amos Korman, Francesco Pasquale, Luca Trevisan, Robin Vacus
We investigate opinion dynamics in a fully-connected system, consisting of $n$ identical and anonymous agents, where one of the opinions (which is called correct) represents a piece of information to disseminate.
1 code implementation • 23 Nov 2022 • Michele Gentili, Leonardo Martini, Marialuisa Sponziello, Luca Becchetti
Motivation: Over the past decade, network-based approaches have proven useful in identifying disease modules within the human interactome, often providing insights into key mechanisms and guiding the quest for therapeutic targets.
no code implementations • 28 Oct 2022 • Leonardo Martini, Adriano Fazzone, Michele Gentili, Luca Becchetti, Brian Hobbs
Results: We present the Relations-Maximization Method, a dense module searching method to identify putative causal genes at GWAS loci through the generation of candidate sub-networks derived by integrating association signals from GWAS data into the gene co-regulation network.
no code implementations • 28 Jul 2022 • Luca Becchetti, Arthur Carvalho Walraven da Cunha, Andrea Clementi, Francesco d'Amore, Hicham Lesfari, Emanuele Natale, Luca Trevisan
random variables $X_1, ..., X_n$, we wish to approximate any point $z \in [-1, 1]$ as the sum of a suitable subset $X_{i_1(z)}, ..., X_{i_s(z)}$ of them, up to error $\varepsilon$.
no code implementations • 8 Jul 2021 • Leonardo Martini, Adriano Fazzone, Luca Becchetti
Background:Typically, proteins perform key biological functions by interacting with each other.
no code implementations • 14 Feb 2020 • Michele Gentili, Leonardo Martini, Manuela Petti, Lorenzo Farina, Luca Becchetti
This work proposes a unified framework to leverage biological information in network propagation-based gene prioritization algorithms.
no code implementations • 31 May 2019 • Aris Anagnostopoulos, Luca Becchetti, Adriano Fazzone, Cristina Menghini, Chris Schwiegelshohn
Reducing hidden bias in the data and ensuring fairness in algorithmic data analysis has recently received significant attention.
1 code implementation • 26 Nov 2018 • Luca Becchetti, Andrea Clementi, Emanuele Natale, Francesco Pasquale, Luca Trevisan
It follows from the Marcus-Spielman-Srivastava proof of the Kadison-Singer conjecture that if $G=(V, E)$ is a $\Delta$-regular dense expander then there is an edge-induced subgraph $H=(V, E_H)$ of $G$ of constant maximum degree which is also an expander.
Distributed, Parallel, and Cluster Computing