Atjaunināt sīkdatņu piekrišanu

E-grāmata: Random Graphs and Complex Networks

(Technische Universiteit Eindhoven, The Netherlands)
  • Formāts - EPUB+DRM
  • Cena: 59,47 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

This rigorous introduction to network science presents random graphs as models for real-world networks. Such networks have distinctive empirical properties and a wealth of new models have emerged to capture them. Classroom tested for over ten years, this text places recent advances in a unified framework to enable systematic study. Designed for a master's-level course, where students may only have a basic background in probability, the text covers such important preliminaries as convergence of random variables, probabilistic bounds, coupling, martingales, and branching processes. Building on this base - and motivated by many examples of real-world networks, including the Internet, collaboration networks, and the World Wide Web - it focuses on several important models for complex networks and investigates key properties, such as the connectivity of nodes. Numerous exercises allow students to develop intuition and experience in working with the models.

Recenzijas

' a modern and deep, yet accessible, introduction to the models that make up [ the] basis for the theoretical study of random graphs and complex networks. The book strikes a balance between providing broad perspective and analytic rigor that is a pleasure for the reader.' Adam Wierman, California Institute of Technology 'This text builds a bridge between the mathematical world of random graphs and the real world of complex networks. It combines techniques from probability theory and combinatorics to analyze the structural properties of large random graphs. Accessible to network researchers from different disciplines, as well as masters and graduate students, the material is suitable for a one-semester course, and is laced with exercises that help the reader grasp the content. The exposition focuses on a number of core models that have driven recent progress in the field, including the ErdsRényi random graph, the configuration model, and preferential attachment models. A detailed description is given of all their key properties. This is supplemented with insightful remarks about properties of related models so that a full panorama unfolds. As the presentation develops, the link to complex networks provides constant motivation for the routes that are being chosen.' Frank den Hollander, Leiden University 'The first volume of Remco van der Hofstad's Random Graphs and Complex Networks is the definitive introduction into the mathematical world of random networks. Written for students with only a modest background in probability theory, it provides plenty of motivation for the topic and introduces the essential tools of probability at a gentle pace. It covers the modern theory of ErdsRényi graphs, as well as the most important models of scale-free networks that have emerged in the last fifteen years. This is a truly wonderful first volume; the second volume, leading up to current research topics, is eagerly awaited.' Peter Mörters, University of Bath 'This new book on random graph models for complex networks is a wonderful addition to the field. It takes the uninitiated reader from the basics of graduate probability to the classical ErdsRényi random graph before terminating at some of the fundamental new models in the discipline. The author does an exemplary job of both motivating the models of interest and building all the necessary mathematical tools required to give a rigorous treatment of these models. Each chapter is complemented by a comprehensive set of exercises allowing the reader ample scope to actively master the techniques covered in the chapter.' Shankar Bhamidi, University of North Carolina, Chapel Hill 'This book is invaluable for anybody who wants to learn or teach the modern theory of random graphs and complex networks. I have used it as a textbook for long and short courses at different levels. Students always like the book because it has all they need: exciting high-level ideas, motivating examples, very clear proofs, and an excellent set of exercises. Easy to read, extremely well structured, and self-contained, the book builds proficiency with random graph models essential for state-of-the-art research.' Nelly Litvak, University of Twente 'What makes the book particularly interesting is that it provides all important preliminaries for readers not having the basic background knowledge of random graphs. The book is well-suited for a graduate course on random graphs, where students may only have minimal background in probability theory, as the book provides plenty of motivation for the topic and covers all important preliminaries. All the chapters are supplemented by extensive exercises to develop better intuition and to progressively master the models covered in the book. In a nutshell, the book is easy to follow and well-organized for developing proficiency in random graph models necessary for state of the art research.' Ghulam Abbas, Complex Adaptive Systems Modeling 'The writing is of a high standard. I would certainly recommend it to a starting graduate student, even if their first degree was not mathematics.' Tobias Muller, Nieuw Archief voor Weskunde

Papildus informācija

This classroom-tested text is the definitive introduction to the mathematics of network science, featuring examples and numerous exercises.
Preface xi
Course Outline xv
1 Introduction
1(54)
1.1 Motivation
1(1)
1.2 Graphs and Their Degree and Connectivity Structure
2(3)
1.3 Complex Networks: the Infamous Internet Example
5(6)
1.4 Scale-Free, Highly Connected and Small-World Graph Sequences
11(5)
1.5 Further Network Statistics
16(4)
1.6 Other Real-World Network Examples
20(20)
1.7 Tales of Tails
40(5)
1.8 Random Graph Models for Complex Networks
45(4)
1.9 Notation
49(1)
1.10 Notes and Discussion
50(2)
1.11 Exercises for
Chapter 1
52(3)
Part I Preliminaries
55(60)
2 Probabilistic Methods
57(30)
2.1 Convergence of Random Variables
57(5)
2.2 Coupling
62(3)
2.3 Stochastic Ordering
65(4)
2.4 Probabilistic Bounds
69(4)
2.5 Martingales
73(5)
2.6 Order Statistics and Extreme Value Theory
78(4)
2.7 Notes and Discussion
82(1)
2.8 Exercises for
Chapter 2
83(4)
3 Branching Processes
87(28)
3.1 Survival versus Extinction
87(4)
3.2 Family Moments
91(1)
3.3 Random-Walk Perspective to Branching Processes
92(4)
3.4 Supercritical Branching Processes
96(4)
3.5 Hitting-Time Theorem and the Total Progeny
100(2)
3.6 Properties of Poisson Branching Processes
102(5)
3.7 Binomial and Poisson Branching Processes
107(2)
3.8 Notes and Discussion
109(2)
3.9 Exercises for
Chapter 3
111(4)
Part II Basic Models
115(62)
4 Phase Transition for the Erdos--Renyi Random Graph
117(33)
4.1 Introduction
117(5)
4.2 Comparisons to Branching Processes
122(2)
4.3 The Subcritical Regime
124(6)
4.4 The Supercritical Regime
130(9)
4.5 CLT for the Giant Component
139(6)
4.6 Notes and Discussion
145(1)
4.7 Exercises for
Chapter 4
146(4)
5 Erdos--Renyi Random Graph Revisited
150(27)
5.1 The Critical Behavior
150(6)
5.2 Critical Erdos--Renyi Random Graphs with Martingales
156(8)
5.3 Connectivity Threshold
164(4)
5.4 Degree Sequence of the Erdos--Renyi Random Graph
168(4)
5.5 Notes and Discussion
172(2)
5.6 Exercises for
Chapter 5
174(3)
Part III Models for Complex Networks
177(124)
Intermezzo: Back to Real-World Networks...
179(4)
6 Generalized Random Graphs
183(33)
6.1 Motivation of the Model
183(1)
6.2 Introduction of the Model
184(6)
6.3 Degrees in the Generalized Random Graph
190(4)
6.4 Degree Sequence of Generalized Random Graph
194(3)
6.5 Generalized Random Graph with I.I.D. Weights
197(2)
6.6 Generalized Random Graph Conditioned on Its Degrees
199(4)
6.7 Asymptotic Equivalence of Inhomogeneous Random Graphs
203(4)
6.8 Related Inhomogeneous Random Graph Models
207(2)
6.9 Notes and Discussion
209(1)
6.10 Exercises for
Chapter 6
210(6)
7 Configuration Model
216(40)
7.1 Motivation for the Configuration Model
216(2)
7.2 Introduction to the Model
218(9)
7.3 Erased Configuration Model
227(5)
7.4 Repeated Configuration Model: Simplicity Probability
232(4)
7.5 Uniform Simple Graphs and Generalized Random Graphs
236(5)
7.6 Configuration Model with I.I.D. Degrees
241(4)
7.7 Related Results on the Configuration Model
245(3)
7.8 Related Random Graph Models
248(2)
7.9 Notes and Discussion
250(2)
7.10 Exercises for
Chapter 7
252(4)
8 Preferential Attachment Models
256(45)
8.1 Motivation for the Preferential Attachment Model
256(3)
8.2 Introduction of the Model
259(3)
8.3 Degrees of Fixed Vertices
262(2)
8.4 Degree Sequences of Preferential Attachment Models
264(3)
8.5 Concentration of the Degree Sequence
267(3)
8.6 Expected Degree Sequence
270(13)
8.7 Maximal Degree in Preferential Attachment Models
283(5)
8.8 Related Results for Preferential Attachment Models
288(2)
8.9 Related Preferential Attachment Models
290(4)
8.10 Notes and Discussion
294(3)
8.11 Exercises for
Chapter 8
297(4)
Appendix 301(3)
Glossary 304(2)
References 306(11)
Index 317
Remco van der Hofstad is Full Professor of Probability at Eindhoven University of Technology and Acting Scientific Director of the European Institute for Statistics, Probability, Stochastic Operations Research and their Applications (Eurandom). He has authored over 100 research articles and has taught courses on random graphs at over ten institutions. He received the 2003 Prix Henri Poincaré (jointly with Gordon Slade) and the 2007 Rollo Davidson Prize, and he is a laureate of the 2003 Innovative Research VIDI Scheme and the 2008 Innovative Research VICI Scheme.