Skip to main content

Maximum-Entropy Networks

Pattern Detection, Network Reconstruction and Graph Combinatorics

  • Book
  • © 2017


  • A complete self-consistent introduction to a general methodology to study complex networks
  • Emphasizes the connections between pattern detection, network reconstruction and graph combinatorics
  • Includes supplementary material:

Part of the book series: SpringerBriefs in Complexity (BRIEFSCOMPLEXITY)

This is a preview of subscription content, log in via an institution to check access.

Access this book

eBook USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

About this book

This book is an introduction to maximum-entropy models of random graphs with given topological properties and their applications. Its original contribution is the reformulation of many seemingly different problems in the study of both real networks and graph theory within the unified framework of maximum entropy. Particular emphasis is put on the detection of structural patterns in real networks, on the reconstruction of the properties of networks from partial information, and on the enumeration and sampling of graphs with given properties. 
After a first introductory chapter explaining the motivation, focus, aim and message of the book, chapter 2 introduces the formal construction of maximum-entropy ensembles of graphs with local topological constraints. Chapter 3 focuses on the problem of pattern detection in real networks and provides a powerful way to disentangle nontrivial higher-order structural features from those that can be traced back to simpler local constraints. Chapter 4 focuses on the problem of network reconstruction and introduces various advanced techniques to reliably infer the topology of a network from partial local information. Chapter 5 is devoted to the reformulation of certain “hard” combinatorial operations, such as the enumeration and unbiased sampling of graphs with given constraints, within a “softened” maximum-entropy framework. A final chapter offers various overarching remarks and take-home messages.
By requiring no prior knowledge of network theory, the book targets a broad audience ranging from PhD students approaching these topics for the first time to senior researchers interested in the application of advanced network techniques to their field.

Similar content being viewed by others


Table of contents (6 chapters)

Authors and Affiliations

  • IMT School for Advanced Studies Lucca, Lucca, Italy

    Tiziano Squartini

  • Lorentz Institute for Theoretical Physics, University of Leiden, Leiden, The Netherlands

    Diego Garlaschelli

Bibliographic Information

Publish with us