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商品コード: 9780198709893

Generating Random Networks and Graphs

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書名

Generating Random Networks and Graphs
著者・編者Coolen, T. et al.
出版社/発行元Oxford University Press
発行年/月2017年3月   
装丁Hardcover
ページ数/巻数 310 ページ
ISBN 978-0-19-870989-3
発送予定海外倉庫よりお取り寄せ 2-3週間以内に発送します

Desciption

 

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.

The book is aimed at the graduate student or advanced undergraduate. It includes many worked examples and open questions making it suitable for use in teaching. Explicit pseudocode algorithms are included throughout the book to make the ideas straightforward to apply.

With larger and larger datasets, it is crucial to have practical and well-understood tools. Being able to test a hypothesis against a properly specified control case is at the heart of the 'scientific method'. Hence, knowledge on how to generate controlled and unbiased random graph ensembles is vital for anybody wishing to apply network science in their research.


Contents:

 

1: Introduction
2: Definitions and concepts
3: Random graph ensembles
4: Soft constraints: exponential random graph models
5: Ensembles with hard constraints
6: Markov Chain Monte Carlo sampling of graphs
7: Graphs with hard constraints: further applications and extensions
8: Network growth algorithms
9: Specific constructions
10: Graphs on structured spaces
11: Application of random graphs

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