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Community Identification in Networks

In this thesis, after introducing some useful shorthand notation that will then be used throughout the text, we analyze a number of methods for community identification that have been proposed in the recent literature.
The thesis is organized as follows: starting from edge removal methods, we move to classical graph theory and spectral partitioning, then apply similar methods to optimize a newly defined objective function known as modularity.
We then turn to other methods for optimizing this objective function, namely Integer Linear Programming (ILP) and greedy approaches such as Simulated Annealing and Agglomerative Hierarchical Clustering (AHC).
Furthermore, we extend the classical definition of modularity, and verify its effectiveness by means of some preliminary studies.
We conclude this work by applying the current state-of-the-art algorithm to the real-world network of scientific collaborations among Italian computer scientists.

Mostra/Nascondi contenuto.

Laurea liv.I

Facoltà: Scienze e Tecnologie Informatiche

Autore: Gianluca Campanella Contatta »

Composta da 41 pagine.


Questa tesi ha raggiunto 28 click dal 26/09/2011.

Disponibile in PDF, la consultazione è esclusivamente in formato digitale.