Detecci\'on de comunidades en redes: Algoritmos y aplicaciones

TL;DR

This thesis analyzes various community detection methods in networks, reviewing their features, classifications, and effectiveness, and evaluates their strengths and weaknesses through different measures.

Contribution

It provides a comprehensive review and classification of community detection algorithms, including their computational complexity and evaluation methods.

Findings

Classification of community detection algorithms

Analysis of algorithm strengths and weaknesses

Evaluation of community quality measures

Abstract

This master's thesis work has the objective of performing an analysis of the methods for detecting communities in networks. As an initial part, I study of the main features of graph theory and communities, as well as common measures in this problem. Subsequently, I was performed a review of the main methods of detecting communities, developing a classification, taking into account its characteristics and computational complexity for the detection of strengths and weaknesses in the methods, as well as later works. Then, study the problem of classification of a clustering method, this in order to evaluate the quality of the communities detected by analyzing different measures. Finally conclusions are elaborated and possible lines of work that can be derived.