Molari, Marco
(2013)

*Implementation of network entropy algorithms on hpc machines, with application to high-dimensional experimental data.*
[Laurea], Università di Bologna, Corso di Studio in

Fisica [L-DM270]

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## Abstract

Network Theory is a prolific and lively field, especially when it approaches Biology. New concepts from this theory find application in areas where extensive datasets are
already available for analysis, without the need to invest money to collect them. The only tools that are necessary to accomplish an analysis are easily accessible: a computing
machine and a good algorithm. As these two tools progress, thanks to technology advancement and human efforts, wider and wider datasets can be analysed.
The aim of this paper is twofold. Firstly, to provide an overview of one of these concepts, which originates at the meeting point between Network Theory and Statistical Mechanics: the entropy of a network ensemble. This quantity has been described from different angles in the literature. Our approach tries to be a synthesis of the different points of view. The second part of the work is devoted to presenting a parallel algorithm that can evaluate this quantity over an extensive dataset. Eventually, the algorithm will also be used to analyse high-throughput data coming from biology.

Abstract

Network Theory is a prolific and lively field, especially when it approaches Biology. New concepts from this theory find application in areas where extensive datasets are
already available for analysis, without the need to invest money to collect them. The only tools that are necessary to accomplish an analysis are easily accessible: a computing
machine and a good algorithm. As these two tools progress, thanks to technology advancement and human efforts, wider and wider datasets can be analysed.
The aim of this paper is twofold. Firstly, to provide an overview of one of these concepts, which originates at the meeting point between Network Theory and Statistical Mechanics: the entropy of a network ensemble. This quantity has been described from different angles in the literature. Our approach tries to be a synthesis of the different points of view. The second part of the work is devoted to presenting a parallel algorithm that can evaluate this quantity over an extensive dataset. Eventually, the algorithm will also be used to analyse high-throughput data coming from biology.

Tipologia del documento

Tesi di laurea
(Laurea)

Autore della tesi

Molari, Marco

Relatore della tesi

Scuola

Corso di studio

Ordinamento Cds

DM270

Parole chiave

network theory, entropy, statistical mechanics, parallel algorithm, c++, programmation, systems biology.

Data di discussione della Tesi

25 Ottobre 2013

URI

## Altri metadati

Tipologia del documento

Tesi di laurea
(Tesi di laurea triennale)

Autore della tesi

Molari, Marco

Relatore della tesi

Scuola

Corso di studio

Ordinamento Cds

DM270

Parole chiave

network theory, entropy, statistical mechanics, parallel algorithm, c++, programmation, systems biology.

Data di discussione della Tesi

25 Ottobre 2013

URI

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