Networks / Mark Newman (University of Michigan).

By:

Newman, M. E. J. (Mark E. J.) [author.]

Material type: Text

TextPublisher: Oxford : Oxford University Press, 2018Edition: Second editionDescription: xi, 780 pages : illustrations (black and white), maps (black and white) ; 25 cmContent type:

text
still image

Media type:

unmediated

Carrier type:

volume

ISBN:

9780198805090

Subject(s):

DDC classification:

003.75 NEW 23

Holdings
Item type	Current library	Call number	Status	Date due	Barcode
Standard Loan	Moylish Library Main Collection	003.75 NEW (Browse shelf(Opens below))	Available		39002100609552

Enhanced descriptions from Syndetics:

The study of networks, including computer networks, social networks, and biological networks, has attracted enormous interest in the last few years. The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on an unprecedented scale, and the development of new theoretical tools has allowed us to extract knowledge from networks of many different kinds. The study of networks is broadlyinterdisciplinary and central developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together themost important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.

Formerly CIP. Uk

Includes bibliographical references and index.

Table of contents provided by Syndetics

Preface (p. ix)
1 Introduction (p. 1)
1 The empirical study of networks (p. 13)
2 Technological networks (p. 14)
2.1 The Internet (p. 15)
2.2 The telephone network (p. 25)
2.3 Power grids (p. 27)
2.4 Transportation networks (p. 28)
2.5 Delivery and distribution networks (p. 29)
3 Networks of information (p. 32)
3.1 The World Wide Web (p. 32)
3.2 Citation networks (p. 37)
3.3 Other information networks (p. 41)
4 Social networks (p. 47)
4.1 The empirical study of social networks (p. 47)
4.2 Interviews and questionnaires (p. 51)
4.3 Direct observation (p. 57)
4.4 Data from archival or third-party records (p. 58)
4.5 Affiliation networks (p. 60)
4.6 The small-world experiment (p. 62)
4.7 Snowball sampling, contact tracing, and random walks (p. 65)
5 Biological networks (p. 70)
5.1 Biochemical networks (p. 70)
5.2 Networks in the brain (p. 88)
5.3 Ecological networks (p. 95)
II Fundamentals of network theory (p. 103)
6 Mathematics of networks (p. 105)
6.1 Networks and their representation (p. 105)
6.2 The adjacency matrix (p. 106)
6.3 Weighted networks (p. 108)
6.4 Directed networks (p. 110)
6.5 Hypergraphs (p. 114)
6.6 Bipartite networks (p. 115)
6.7 Multilayer and dynamic networks (p. 118)
6.8 Trees (p. 121)
6.9 Planar networks (p. 123)
6.10 Degree (p. 126)
6.11 Walks and paths (p. 131)
6.12 Components (p. 133)
6.13 Independent paths, connectivity, and cut sets (p. 137)
6.14 The graph Laplacian (p. 142)
7 Measures and metrics (p. 158)
7.1 Centrality (p. 159)
7.2 Groups of nodes (p. 177)
7.3 Transitivity and the clustering coefficient (p. 183)
7.4 Reciprocity (p. 189)
7.5 Signed edges and structural balance (p. 190)
7.6 Similarity (p. 194)
7.7 Homophily and assortative mixing (p. 201)
8 Computer algorithms (p. 218)
8.1 Software for network analysis and visualization (p. 219)
8.2 Running time and computational complexity (p. 221)
8.3 Storing network data (p. 225)
8.4 Algorithms for basic network quantities (p. 237)
8.5 Shortest paths and breadth-first search (p. 241)
8.6 Shortest paths in networks with varying edge lengths (p. 257)
8.7 Maximum flows and minimum cuts (p. 262)
9 Network statistics and measurement error (p. 275)
9.1 Types of error (p. 276)
9.2 Sources of error (p. 278)
9.3 Estimating errors (p. 281)
9.4 Correcting errors (p. 297)
10 The structure of real-world networks (p. 304)
10.1 Components (p. 304)
10.2 Shortest paths and the small-world effect (p. 310)
10.3 Degree distributions (p. 313)
10.4 Power laws and scale-free networks (p. 317)
10.5 Distributions of other centrality measures (p. 330)
10.6 Clustering coefficients (p. 332)
10.7 Assortative mixing (p. 335)
III Network models (p. 341)
11 Random graphs (p. 342)
11.1 Random graphs (p. 343)
11.2 Mean number of edges and mean degree (p. 345)
11.3 Degree distribution (p. 346)
11.4 Clustering coefficient (p. 347)
11.5 Giant component (p. 347)
11.6 Small components (p. 355)
11.7 Path lengths (p. 360)
11.8 Problems with the random graph (p. 364)
12 The configuration model (p. 369)
12.1 The configuration model (p. 370)
12.2 Excess degree distribution (p. 377)
12.3 Clustering coefficient (p. 381)
12.4 Locally tree-like networks (p. 382)
12.5 Number of second neighbors of a node (p. 383)
12.6 Giant component (p. 384)
12.7 Small components (p. 391)
12.8 Networks with power-law degree distributions (p. 395)
12.9 Diameter (p. 399)
12.10 Generating function methods (p. 401)
12.11 Other random graph models (p. 416)
13 Models of network formation (p. 434)
13.1 Preferential attachment (p. 435)
13.2 The model of Barabási and Albert (p. 448)
13.3 Time evolution of the network and the first mover effect (p. 451)
13.4 Extensions of preferential attachment models (p. 458)
13.5 Node copying models (p. 472)
13.6 Network optimization models (p. 479)
IV Applications (p. 493)
14 Community structure (p. 494)
14.1 Dividing networks into groups (p. 495)
14.2 Modularity maximization (p. 498)
14.3 Methods based on information theory (p. 515)
14.4 Methods based on statistical inference (p. 520)
14.5 Other algorithms for cornrnunity detection (p. 529)
14.6 Measuring algorithm performance (p. 538)
14.7 Detecting other kinds of network structure (p. 551)
15 Percolation and network resilience (p. 569)
15.1 Percolation (p. 569)
15.2 Uniform random removal of nodes (p. 571)
15.3 Non-uniform removal of nodes (p. 586)
15.4 Percolation in real-world networks (p. 593)
15.5 Computer algorithms for percolation (p. 594)
16 Epidemics on networks (p. 607)
16.1 Models of the spread of infection (p. 608)
16.2 Epidemic models on networks (p. 624)
16.3 Outbreak sizes and percolation (p. 625)
16.4 Time-dependent properties of epidemics on networks (p. 645)
16.5 Time-dependent properties of the SI model (p. 646)
16.6 Time-dependent properties of the SIR model (p. 660)
16.7 Time-dependent properties of the SIS model (p. 667)
17 Dynamical systems on networks (p. 675)
17.1 Dynamical systems (p. 676)
17.2 Dynamics on networks (p. 685)
17.3 Dynamics with more than one variable per node (p. 694)
17.4 Spectra of networks (p. 698)
17.5 Synclironization (p. 701)
18 Network search (p. 710)
18.1 Web search (p. 710)
18.2 Searching distributed databases (p. 713)
18.3 Sending messages (p. 718)
References (p. 732)
Index (p. 751)

Author notes provided by Syndetics

Mark Newman is the Anatol Rapoport Distinguished University Professor of Physics at the University of Michigan.