Autor: Béla Bollobás, Robert Kozma, Dezso Miklós
Herausgeber: Bela Bollobas, Robert Kozma, Dezs Miklós
Titel: Handbook of Large-Scale Random Networks
Verlag: Springer-Verlag
ISBN/ISSN: 9783540693956
Auflage: 1
Preis : CHF 135.50
Kategorie: Informatik, EDV Buch
Sprache: English
Technische Daten
Seiten: 600
Kopierschutz: DRM
Geräte: PC/MAC/eReader/Tablet
Formate: PDF
With the advent of digital computers more than half a century ago, - searchers working in a wide range of scienti?c disciplines have obtained an extremely powerful tool to pursue deep understanding of natural processes in physical, chemical, and biological systems. Computers pose a great ch- lenge to mathematical sciences, as the range of phenomena available for rigorous mathematical analysis has been enormously expanded, demanding the development of a new generation of mathematical tools. There is an explosive growth of new mathematical disciplines to satisfy this demand, in particular related to discrete mathematics. However, it can be argued that at large mathematics is yet to provide the essential breakthrough to meet the challenge. The required paradigm shift in our view should be compa- ble to the shift in scienti?c thinking provided by the Newtonian revolution over 300 years ago. Studies of large-scale random graphs and networks are critical for the progress, using methods of discrete mathematics, probabil- tic combinatorics, graph theory, and statistical physics. Recent advances in large scale random network studies are described in this handbook, which provides a signi?cant update and extension - yond the materials presented in the 'Handbook of Graphs and Networks' published in 2003 by Wiley. The present volume puts special emphasis on large-scale networks and random processes, which deemed as crucial for - tureprogressinthe?eld. Theissuesrelatedtorandomgraphsandnetworks pose very di?cult mathematical questions.
"Chapter 1 Random Graphs and Branching Processes (p. 15-16)


During the past decade or so, there has been much interest in generating and analyzing graphs resembling large-scale real-world networks such as the world wide web, neural networks, and social networks. As these large-scale networks seem to be ‘random’, in the sense that they do not have a transparent, well-de?ned structure, it does not seem too unreasonable to hope to ?nd classical models of random graphs that share their basic properties.

Such hopes are quickly dashed, however, since the classical random graphs are all homogeneous, in the sense that all vertices (or indeed all k-sets of vertices) are a priori equivalent in the model. Most real-world networks are not at all like this, as seen most easily from their often unbalanced (power-law) degree sequences. Thus, in order to model such graphs, a host of inhomogeneous random graph models have been constructed and studied.

In this paper we shall survey a number of these models and the basic results proved about the inhomogeneous sparse (bounded average degree) random graphs they give rise to. We shall focus on mathematically tractable models, which often means models with independence between edges, and in particular on the very general sparse inhomogeneous models of Bollob´as, Janson and Riordan. The ?rst of these encompasses a great range of earlier models of this type; the second, the inhomogeneous clustering model, goes much further, allowing for the presence of clustering while retaining tractability.

We are not only interested in our inhomogeneous random graphs themselves, but also in the random subgraphs obtained by keeping their edges with a certain probability p. Our main interest is in the phase transition that takes place around a certain critical value p0 of p, when the component structure of the random subgraph undergoes a sudden change. The quintessential phase transition occurs in the classical binomial random graph G(n, c/n) as c grows from less than 1 to greater than 1 and, as shown by Erd?os and R´enyi, a unique largest component, the giant component, is born.

A ubiquitous theme of our paper is the use of branching processes in the study of random graphs. This ‘modern’ approach to random graphs is crucial in the study of the very general models of inhomogeneous random graphs mentioned above. To illustrate the power of branching processes, we show how they can be used to reprove sharp results about the classical random graph G(n, c/n), ?rst proved by Bollob´as and Luczak over twenty years ago. When it comes to inhomogeneous models, we shall have time only to sketch the connection to branching processes. Finally, we close by discussing the question of how to tell whether a given model is appropriate in a given situation. This leads to many fascinating questions about metrics for sparse graphs, and their relationship to existing models and potential new models."
Title Page4
Copyright Page5
Table of Contents6
Chapter 1 Random Graphs and Branching Processes16
1. Introduction17
2. Models20
2.1. Classical models20
2.2. Random graphs with a fixed degree sequence24
2.3. Inhomogeneous models27
2.4. Models with independence between edges28
2.5. A general sparse inhomogeneous model30
2.6. Independence and clustering33
2.7. Further models34
3. The Phase Transition in G(n, p)35
3.1. Historical remarks36
3.2. Local behaviour41
3.3. The giant component44
3.4. Stronger results for G(n, p)47
3.5. The subcritical case51
3.6. The supercritical case61
4. The Phase Transition in Inhomogeneous Random Graphs69
4.1. Graphs with a given degree sequence69
4.2. Robustness of the BA or LCD model71
4.3. The uniformly grown models and Dubins model73
4.4. Graphs with independence between edges77
4.5. Applications of non-Poisson branching processes80
5. Branching Processes and Other Global Properties85
5.1. Diameter85
5.2. The k-core88
6. Appropriateness of Models: Metrics on Graphs91
6.1. The edit distance(s): dense case92
6.2. The subgraph distance: dense case95
6.3. The cut metric: dense case97
6.4. The sparse case99
6.5. New metrics and models103
Chapter 2 Percolation, Connectivity, Coverage and Colouring of Random Geometric Graphs117
1. Introduction117
2. The Gilbert Disc Model118
2.1. Percolation119
2.2. Connectivity120
2.3. Coverage121
2.4. Colouring123
2.5. Thin strips125
3. The k-nearest Neighbour Model128
3.1. Percolation128
3.2. Connectivity129
3.3. Sharp thresholds131
4. Random Tessellations132
4.1. Random Voronoi Percolation133
4.2. Random JohnsonMehl Percolation137
5. Outlook138
Chapter 3 Scaling Properties of Complex Networks and Spanning Trees143
1. Random Graphs and Complex Networks143
2. The Bollobás Configuration Model145
3. Percolation on Random Graphs146
3.1. Generating functions147
3.2. Critical exponents149
3.3. Fractal dimensions151
4. Weighted Networks154
4.1. Shortest paths in networks154
4.2. Strong and Weak Disorder155
4.3. Minimum Spanning Trees156
4.4. Fractal properties of optimal paths158
5. Fractal Networks160
5.1. The cluster growing method160
5.2. The box covering method160
5.3. The fractal nature of scale free networks161