4 edition of **Structural analysis of complex networks** found in the catalog.

- 50 Want to read
- 26 Currently reading

Published
**2011**
by Birkhäuser in Dordrecht, New York
.

Written in English

- System analysis,
- Graph theory

**Edition Notes**

Includes bibliographical references and index.

Statement | Matthias Dehmer, editor |

Classifications | |
---|---|

LC Classifications | QA166 .S85 2011 |

The Physical Object | |

Pagination | xiii, 486 p. : |

Number of Pages | 486 |

ID Numbers | |

Open Library | OL25093887M |

ISBN 10 | 0817647880, 0817647899 |

ISBN 10 | 9780817647889, 9780817647896 |

LC Control Number | 2010938359 |

OCLC/WorldCa | 320493864 |

In the context of network theory, a complex network is a graph (network) with non-trivial topological features—features that do not occur in simple networks such as lattices or random graphs but often occur in graphs modelling of real systems. The study of complex networks is a young and active area of scientific research (since ) inspired largely by the empirical study of real-world. Affiliations. Center for Complex Network Research and Departments of Physics, Computer Science and Biology, Northeastern University, Boston, , Massachusetts, USA.

information network. Given a complex heterogeneous information network, it is necessary to provide its meta level (i.e., schema-level) de-scription for better understanding the object types and link types in the network. Therefore, we propose the concept of network schema to describe the meta structure of a network. Definition 2. Uncovering the community structure is crucial to the understanding of the structural and functional properties of realworld complex networks. As there is no universal definition of a community.

applications for analysis of complex dynamic networks. Planned topics •short introduction to complex networks •complex networks, definitions, basics •Graph partition • min-cut, normalized-cut, min-ratio-cut - structural equivalence: share the same neighbors => Jaccard coe cientﬃ. Egocentric network analysis requires a unique set of data collection and analysis skills that overlap only minimally with other network methodologies. However, until now there has been no single reference for conceptualizing, collecting, and analyzing egocentric social network data.

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From the reviews: “The book Structural Analysis of Complex Networks presents theoretical as well as practice-oriented results for structurally exploring networks, combining graph-theoretic methods with mathematical techniques from other scientific disciplines such as machine learning, statistics and information theory.

the book is addressed to an interdisciplinary audience, covering. Structural Analysis of Complex Networks is suitable for a broad, interdisciplinary readership of researchers, practitioners, and graduate students in discrete mathematics, statistics, computer science, machine learning, artificial intelligence, computational and systems biology, cognitive science, computational linguistics, and mathematical.

The book may be used as a supplementary textbook in graduate-level seminars on structural graph analysis, complex networks, or network-based machine learning methods. If the item details above aren’t accurate or complete, we want to know about : $ Filling a gap in literature, this self-contained book presents theoretical and application-oriented results that allow for a structural exploration of complex networks.

The work focuses not only on classical graph-theoretic methods, but also demonstrates the usefulness of structural graph theory as a tool for solving interdisciplinary problems. The book may be used as a supplementary textbook in graduate-level seminars on structural graph analysis, complex networks, or network-based machine learning methods.

Categories: Computers\\Networking. Download Citation | Structural Analysis of Complex Networks | Because of the increasing complexity and growth of real-world networks, their analysis by using classical graph-theoretic methods is Author: Matthias Dehmer.

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.

Introduction to Complex Networks: Structure and Dynamics Ernesto Estrada 1 Introduction section, most of the structural properties of these networks are determined by this information only.

In contrast, to describe the structure of one of the networks An important concept in the analysis of networks. The design of tall buildings and complex structures involves challenging activities, including: scheme design, modelling, structural analysis and detailed design. This book provides structural designers with a systematic approach to anticipate and solve issues for tall buildings and complex structures.

This book begins with a clear and rigorous. From the reviews:"The book Structural Analysis of Complex Networks presents theoretical as well as practice-oriented results for structurally exploring networks, combining graph-theoretic methods (Sanzaiana Caraman, IASI Polytechnic Magazine, Vol.

22 (1/4), March-December, ) Read more. springer, Because of the increasing complexity and growth of real-world networks, their analysis by using classical graph-theoretic methods is oftentimes a difficult procedure.

As a result, there is a strong need to combine graph-theoretic methods with mathematical techniques from other scientific disciplines, such as machine learning and information theory, in order to analyze complex.

We view interconnected, multityped data, including the typical relational database data, as heterogeneous information networks, study how to leverage the rich semantic meaning of structural types of objects and links in the networks, and develop a structural analysis approach on mining semi-structured, multi-typed heterogeneous information.

Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization.

The brain's structural. 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.

It is divided into two parts consisting of twelve chapters dedicated to the development of theoretic tools for the. 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. Free 2-day shipping on qualified orders over $ Buy Structural Analysis of Complex Networks (Hardcover) at Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore.

You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. 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 Cited by: Explore the multidisciplinary nature of complex networks through machine learning techniques.

Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on.

Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization.

The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain.

This is mainly because the local and global properties of complex networks are useful for recognizing complicated interconnections and the information flow between different components in extended systems.

The adjacency matrix, which describes the relations between the nodes of a network, is the basis of complex network analysis.Advanced Material: Analysis of Rich-Get-Richer Processes Part VI Network Dynamics: Structural Models Chapter Cascading Behavior in Networks.

Diffusion in Networks Modeling Diffusion through a Network Cascades and Clusters. 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/5(6).