Detección de comunidades en redes: Algoritmos y aplicaciones

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.

Master's Thesis

References in corpus (8)

• Uncovering the overlapping community structure of complex networks in nature and society
• Benchmark graphs for testing community detection algorithms
• Comparing community structure identification
• Detecting the overlapping and hierarchical community structure of complex networks
• Detect overlapping and hierarchical community structure in networks
• A sequential algorithm for fast clique percolation
• A Novel Measure of Edge Centrality in Social Networks
• Enhancing community detection using a network weighting strategy