Generating Random Networks and Graphs - Hardcover

Coolen, Ton; Annibale, Alessia; Roberts, Ekaterina

Hardcover

ISBN 10: 0198709897 ISBN 13: 9780198709893

Verlag: OUP Oxford, 2017

Inhaltsangabe

Generating random networks and graphs editado por

Über diesen Titel

Críticas:

It succeeds to reach a high degree of learning osmosis for the reader; moreover, relative transmission of research experience, to name a few advantages of the volume at hand. The authors succeed in doing many things well, e.g. inventing a lot of suitable paradigms. (Nikolaos E. Myridis, Contemporary Physics)

This is a magnificent guide through a subject that is deeper, more subtle and much more important for data-based applications than one might suspect, and it fully reflects the authors' technical prowess and teaching abilities. Advanced readers will find much food for thought in these pages. (Andrea De Martino, National Research Council of Italy & Human Genetics Foundation)

Reseña del editor:

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.

Über diese Auflage

• Verlag: OUP Oxford
• Erscheinungsdatum: 2017
• ISBN 10: 0198709897
• ISBN 13: 9780198709893
• Einband: Tapa dura
• Anzahl der Seiten: 324