Data science and complex networks real cases studies with Python 1st by Caldarelli Chessa 0191024023 9780199639601

ISBN 10: 0191024023

ISBN 13: 9780199639601

Author: Caldarelli , Chessa

This book provides a comprehensive yet short description of the basic concepts of Complex Network theory. In contrast to other books the authors present these concepts through real case studies. The application topics span from Foodwebs, to the Internet, the World Wide Web and the Social Networks, passing through the International Trade Web and Financial time series. The final part is devoted to definition and implementation of the most important network models. The text provides information on the structure of the data and on the quality of available datasets. Furthermore it provides a series of codes to allow immediate implementation of what is theoretically described in the book. Readers already used to the concepts introduced in this book can learn the art of coding in Python by using the online material. To this purpose the authors have set up a dedicated web site where readers can download and test the codes. The whole project is aimed as a learning tool for scientists and practitioners, enabling them to begin working instantly in the field of Complex Networks.

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 component

4 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 networks