Detección de comunidades en redes: Algoritmos y aplicaciones
arXiv:2009.08390 · doi:10.13140/RG.2.2.15213.82400
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.
arXiv commentMaster's Thesis
References in corpus (18)
- The structure and function of complex networks
- Community structure in social and biological networks
- Finding and evaluating community structure in networks
- Community detection in graphs
- Finding community structure in very large networks
- Natural Scales in Geographical Patterns
- Fast algorithm for detecting community structure in networks
- Uncovering the overlapping community structure of complex networks in nature and society
- Benchmark graphs for testing community detection algorithms
- Comparing community structure identification
- Characterization of complex networks: A survey of measurements
- Defining and identifying communities in networks
- Detecting the overlapping and hierarchical community structure of complex networks
- Detect overlapping and hierarchical community structure in networks
- Detecting fuzzy community structures in complex networks with a Potts model
- A sequential algorithm for fast clique percolation
- A Novel Measure of Edge Centrality in Social Networks
- Enhancing community detection using a network weighting strategy