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Data science and complex networks : real cases studies with Python /
This work guides the reader in the analysis of big-data by providing theoretical and practical instruments to tame the complexity of such systems. Together with support provided by the companion website, it constitutes a simple and useful handbook for data analysts.
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Oxford, United Kingdom ; New York, NY :
Oxford University Press,
2016.
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| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Cover; Preface; Contents; Introduction; 1 Food Webs; 1.1 Introduction; 1.2 Data from EcoWeb and foodweb.org; 1.3 Store and measure a graph: size, measure, and degree; 1.4 Degree sequence; 1.5 Clustering coefficient and motifs; 2 International Trade Networks and World Trade Web; 2.1 Introduction; 2.2 Data from COMTRADE; 2.3 Projecting and symmetrising a bipartite network; 2.4 Neighbour quantities: reciprocity and assortativity; 2.5 Multigraphs; 2.6 The bipartite network of products and countries; 3 The Internet Network; 3.1 Introduction; 3.2 Data from CAIDA; 3.3 Importance or centrality
- 3.4 Robustness and resilience, giant component4 World Wide Web, Wikipedia, and Social Networks; 4.1 Introduction; 4.2 Data from various sources; 4.3 Bringing order to the WWW; 4.4 Communities and Girvan-Newman algorithm; 4.5 Modularity; 5 Financial Networks; 5.1 Introduction; 5.2 Data from Yahoo! Finance; 5.3 Prices time series; 5.4 Correlation of prices; 5.5 Minimal spanning trees; 6 Modelling; 6.1 Introduction; 6.2 Exponential growth, chains, and random graph; 6.3 Random graphs; 6.4 Configuration models; 6.5 Gravity model; 6.6 Fitness model; 6.7 Barabási-Albert model
- 6.8 Reconstruction of networksReferences; Index