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Published **2009**
by Springer in Berlin .

Written in English

- Cluster analysis,
- Data mining,
- Pattern recognition systems,
- Graph theory,
- Statistical physics

**Edition Notes**

Includes bibliographical references.

Statement | J. Reichardt. |

Series | The lecture notes in physics -- 766, Lecture notes in physics -- 766. |

Classifications | |
---|---|

LC Classifications | QA278 .R445 2009 |

The Physical Object | |

Pagination | xiii, 151 p. : |

Number of Pages | 151 |

ID Numbers | |

Open Library | OL24022208M |

ISBN 10 | 3540878327, 3540878335 |

ISBN 10 | 9783540878322, 9783540878339 |

LC Control Number | 2008936629 |

This concise and pedagogical research monograph introduces the reader to two specific aspects - clustering techniques and dimensionality reduction - in the context of complex network analysis. The first chapter provides a short introduction into relevant graph theoretical notation; chapter 2 then reviews and compares a number of cluster definitions from different fields of science. This concise and pedagogical research monograph introduces the reader to two specific aspects - clustering techniques and dimensionality reduction - in the context of complex network analysis. The first chapter provides a short introduction into relevant graph theoretical notation; chapter 2 then reviews and compares a number of cluster definitions from different fields of by: This book deals with the analysis of the structure of complex networks by combining results from graph theory, physics, and pattern recognition. The book is divided into two parts. 11 chapters are dedicated to the development of theoretical tools for the structural analysis of networks, and 7 chapters are illustrating, in a critical way, applications of these tools to real-world scenarios. This book is devoted to the analysis of the structure of complex networks by combining results from algebraic, topological, and extremal graph theory with statistical and molecular physics, as well as with contributions from mathematical chemistry, biology, and social sciences.

This book deals with the analysis of the structure of complex networks by combining results from graph theory, physics, and pattern recognition. Community structure is a salient structural characteristic of many real-world networks. Communities are generally hierarchical, overlapping, multi-scale and coexist with other types of structural regularities of networks. This poses major challenges for conventional methods of community detection. This book will comprehensively introduce the latest advances in community detection, especially the detection of overlapping and hierarchical community structures Brand: Springer-Verlag Berlin Heidelberg. Structure in complex networks. [J Reichardt] -- "In the modern world of gigantic datasets, which scientists and practioners of all fields of learning are confronted with, the availability of robust, scalable and easy-to-use methods for pattern Your Web browser is not enabled for JavaScript. Some features of WorldCat will not be available. Introduction to Complex Networks: Structure and Dynamics Fig. 4Representation of the variation in the average path length and clustering coefﬁcient with the change of the rewiring probability in the Watts-Strogatz model with nodes and 5, by:

() Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure. International Journal of Modern Physics B , () The three kinds of degree distributions and nash equilibrium on the limiting random network. M. E. J. NEWMAN (c) (b) (d) (a) Fig Examples of various types of networks: (a) an undirected network with only a single type of vertex and a single type of edge; (b) a network with a number of discrete vertex and edge types; (c) a network with varying vertex and edge weights; (d) a directed network in which each edge has a direction. 2 The structure and function of complex networks I. INTRODUCTION Anetworkisasetofitems,whichwewillcallvertices or sometimes nodes, with connections between them, called edges(Fig. 1). Systems taking the form of net-works(alsocalled\graphs"inmuchofthemathematical literature)aboundintheworld. ExamplesincludetheIn-. Community structure is a salient structural characteristic of many real-world networks. Communities are generally hierarchical, overlapping, multi-scale and coexist with other types of structural regularities of networks. This poses major challenges for conventional methods of community detection. This book will comprehensively introduce the latest advances in community detection, especially the detection of .

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