Search Results for author: Adriano Fazzone

Found 6 papers, 0 papers with code

A Survey on the Densest Subgraph Problem and Its Variants

no code implementations25 Mar 2023 Tommaso Lanciano, Atsushi Miyauchi, Adriano Fazzone, Francesco Bonchi

The Densest Subgraph Problem requires to find, in a given graph, a subset of vertices whose induced subgraph maximizes a measure of density.

Network Based Approach to Gene Prioritization at Genome-Wide Association Study Loci

no code implementations28 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.

Network and Sequence-Based Prediction of Protein-Protein Interactions

no code implementations8 Jul 2021 Leonardo Martini, Adriano Fazzone, Luca Becchetti

Background:Typically, proteins perform key biological functions by interacting with each other.

Algorithms for Hiring and Outsourcing in the Online Labor Market

no code implementations16 Feb 2020 Aris Anagnostopoulos, Carlos Castillo, Adriano Fazzone, Stefano Leonardi, Evimaria Terzi

In this paper, we provide algorithms for outsourcing and hiring workers in a general setting, where workers form a team and contribute different skills to perform a task.

Algorithms for Fair Team Formation in Online Labour Marketplaces

no code implementations14 Feb 2020 Giorgio Barnabò, Adriano Fazzone, Stefano Leonardi, Chris Schwiegelshohn

In this short paper, we define the Fair Team Formation problem in the following way: given an online labour marketplace where each worker possesses one or more skills, and where all workers are divided into two or more not overlapping classes (for examples, men and women), we want to design an algorithm that is able to find a team with all the skills needed to complete a given task, and that has the same number of people from all classes.

Fairness

Principal Fairness: Removing Bias via Projections

no code implementations31 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.

Clustering Fairness

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