This text provides a comprehensive and concise description of the basic concepts of Complex Network theory. It focuses on the scientific opportunities provided by the appearance of big-data and describes how to exploit them using the state-of-the-art theoretical approach of complex networks. Starting from specific examples and available data, this book focuses on the best way to extract information. In contrast to other books it presents these concepts beginning with real case studies, provides information on the structure of the data and on the quality of available datasets, and provides a series of codes allowing readers to implement instantly what is theoretically described in the book. Chapter by chapter the book introduces both the theoretical definition of the instruments used and the codes to apply these ideas to data. In this way, the book represents both a didactical textbook for students and a handbook for scientists and practitioners. Readers already used to the concepts introduced in this book can even learn the art of coding in Python by using the available online material. The authors have set up a dedicated web site where readers can download and test the codes.
Arvustused
Data science and network science are two of the most dynamically developing areas in modern science. It is fantastic to see these two topics, whose synergy is evident to the practitioner, under one roof, presented with clarity and through numerous practical examples by Caldarelli and Chessa. * Albert-László Barabási, Northeastern University * The authors nicely integrate ideas from data science and complex networks to create a toolkit for tackling big data challenges. An essential read in the information age. * Geoff F. Rodgers, Brunel University London *
| Introduction |
|
1 | (3) |
|
|
|
4 | (22) |
|
|
|
4 | (2) |
|
1.2 Data from EcoWeb and foodweb.org |
|
|
6 | (2) |
|
1.3 Store and measure a graph: size, measure, and degree |
|
|
8 | (6) |
|
|
|
14 | (1) |
|
1.5 Clustering coefficient and motifs |
|
|
15 | (11) |
|
2 International Trade Networks and World Trade Web |
|
|
26 | (19) |
|
|
|
26 | (1) |
|
|
|
27 | (3) |
|
2.3 Projecting and symmetrising a bipartite network |
|
|
30 | (3) |
|
2.4 Neighbour quantities: reciprocity and assortativity |
|
|
33 | (4) |
|
|
|
37 | (3) |
|
2.6 The bipartite network of products and countries |
|
|
40 | (5) |
|
|
|
45 | (18) |
|
|
|
45 | (1) |
|
|
|
45 | (5) |
|
3.3 Importance or centrality |
|
|
50 | (6) |
|
3.4 Robustness and resilience, giant component |
|
|
56 | (7) |
|
4 World Wide Web, Wikipedia, and Social Networks |
|
|
63 | (28) |
|
|
|
63 | (1) |
|
4.2 Data from various sources |
|
|
64 | (6) |
|
4.3 Bringing order to the WWW |
|
|
70 | (12) |
|
4.4 Communities and Girvan-Newman algorithm |
|
|
82 | (3) |
|
|
|
85 | (6) |
|
|
|
91 | (12) |
|
|
|
91 | (1) |
|
5.2 Data from Yahoo! Finance |
|
|
91 | (5) |
|
|
|
96 | (2) |
|
5.4 Correlation of prices |
|
|
98 | (1) |
|
5.5 Minimal spanning trees |
|
|
99 | (4) |
|
|
|
103 | (19) |
|
|
|
103 | (1) |
|
6.2 Exponential growth, chains, and random graph |
|
|
103 | (4) |
|
|
|
107 | (3) |
|
|
|
110 | (2) |
|
|
|
112 | (2) |
|
|
|
114 | (1) |
|
6.7 Barabasi-Albert model |
|
|
115 | (2) |
|
6.8 Reconstruction of networks |
|
|
117 | (5) |
| References |
|
122 | (7) |
| Index |
|
129 | |
Guido Caldarelli received his Ph.D. from SISSA (Italy), after which he was a postdoc in the University of Manchester (UK). He then worked at the TCM Group, University of Cambridge (UK), He returned to Italy as a lecturer at National Institute for Condensed Matter (INFM) and later as Primo Ricercatore in the Institute of Complex Systems of the National Research Council (CNR) of Italy. He also spent some terms at University of Fribourg (Switzerland) and he has been visiting professor at ENS in Paris, University of Barcelona and ETH Zurich. He is expert of Statistical Physics and Complex Networks and author of more than 150 publications and two books on the topic. He is currently oordinating the EC FET IP project Multiplex on Multi-level complex systems.
Alessandro Chessa graduated in Physics and received a PhD in theoretical Physics at the University of Cagliari (Italy). From April 1999 to July 2000 he has been Research Associate in the Physics Department of Boston University, studying Econophysics. In the meantime he has also been Scientific Consultant at the International Center for Theoretical Physics (ICTP, Trieste) for a project about Parallel Computation. In the year 2012 he has been adjunct researcher in the Institute for Complex Systems (CNR) , 'La Sapienza' Rome, doing research in the field of Complex Network Theory. At present he is Assistant Professor in Statistical Physics in IMT, Institute of Advanced Studies, Lucca (Italy). Expert of Complex Networks and Data Science, has worked in the area of Community Detection for spatial networks. As entrepreneur is the founder of the SME Linkalab.
Lisainfo e-raamatute kohta