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Product details:
- ISBN 10: 019101981X
- ISBN 13: 9780191019814
- Author: Annibale
Generating random networks efficiently and accurately is an important challenge for practical applications, and an interesting question for theoretical study. This book presents and discusses common methods of generating random graphs. It begins with approaches such as Exponential Random Graph Models, where the targeted probability of each network appearing in the ensemble is specified. This section also includes degree-preserving randomisation algorithms, where the aim is to generate networks with the correct number of links at each node, and care must be taken to avoid introducing a bias. Separately, it looks at growth style algorithms (e.g. preferential attachment) which aim to model a real process and then to analyse the resulting ensemble of graphs. It also covers how to generate special types of graphs including modular graphs, graphs with community structure and temporal graphs.
Table of contents:
Part I The basics
1 Introduction
2 Definitions and concepts
3 Random graph ensembles
Part II Random graph ensembles
4 Soft constraints: exponential random graph models
5 Ensembles with hard constraints
Part III Generating graphs from graph ensembles
6 Markov Chain Monte Carlo sampling of graphs
7 Graphs with hard constraints: further applications and extensions
Part IV Graphs defined by algorithms
8 Network growth algorithms
9 Specific constructions
Part V Further topics
10 Graphs on structured spaces
11 Applications of random graphs
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