Community Structure of Complex Networks
Community Structure of Complex Networks

Author: Hua-Wei Shen

Publisher: Springer Science & Business Media

Published: 2013-01-06

Total Pages: 128

ISBN-13: 3642318215

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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, the detection of multi-scale communities in heterogeneous networks, and the exploration of multiple types of structural regularities. These advances have been successfully applied to analyze large-scale online social networks, such as Facebook and Twitter. This book provides readers a convenient way to grasp the cutting edge of community detection in complex networks. The thesis on which this book is based was honored with the “Top 100 Excellent Doctoral Dissertations Award” from the Chinese Academy of Sciences and was nominated as the “Outstanding Doctoral Dissertation” by the Chinese Computer Federation.

Machine Learning and Knowledge Discovery in Databases
Machine Learning and Knowledge Discovery in Databases

Author: Walter Daelemans

Publisher: Springer Science & Business Media

Published: 2008-09-04

Total Pages: 714

ISBN-13: 354087478X

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This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.

Statistical Mechanics of Complex Networks
Statistical Mechanics of Complex Networks

Author: Romualdo Pastor-Satorras

Publisher: Springer Science & Business Media

Published: 2003-08-08

Total Pages: 232

ISBN-13: 9783540403722

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Networks can provide a useful model and graphic image useful for the description of a wide variety of web-like structures in the physical and man-made realms, e.g. protein networks, food webs and the Internet. The contributions gathered in the present volume provide both an introduction to, and an overview of, the multifaceted phenomenology of complex networks. Statistical Mechanics of Complex Networks also provides a state-of-the-art picture of current theoretical methods and approaches.

Large Scale Structure And Dynamics Of Complex Networks: From Information Technology To Finance And Natural Science
Large Scale Structure And Dynamics Of Complex Networks: From Information Technology To Finance And Natural Science

Author: Alessandro Vespignani

Publisher: World Scientific

Published: 2007-06-28

Total Pages: 264

ISBN-13: 9814475416

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This book is the culmination of three years of research effort on a multidisciplinary project in which physicists, mathematicians, computer scientists and social scientists worked together to arrive at a unifying picture of complex networks. The contributed chapters form a reference for the various problems in data analysis visualization and modeling of complex networks.

The Stanford GraphBase
The Stanford GraphBase

Author: Donald Ervin Knuth

Publisher: Addison-Wesley Professional

Published: 2009

Total Pages: 0

ISBN-13: 9780321606327

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The Stanford GraphBase: A Platform for Combinatorial Computing represents the first efforts of Donald E. Knuth's preparation for Volume Four of The Art of Computer Programming. The book's first goal is to use examples to demonstrate the art of literate programming. Each example provides a programmatic essay that can be read and enjoyed as readily as it can be interpreted by machines. In these essays/programs, Knuth makes new contributions to several important algorithms and data structures, so the programs are of special interest for their content as well as for their style. The book's second goal is to provide a useful means for comparing combinatorial algorithms and for evaluating methods of combinatorial computing. To this end, Knuth's programs offer standard, freely available sets of data - the Stanford GraphBase - that may be used as benchmarks to test competing methods. The data sets are both interesting in themselves and applicable to a wide variety of problem domains. With objective tests, Knuth hopes to bridge the gap between theoretical computer scientists and programmers who have real problems to solve. As with all of Knuth's writings, this book is appreciated not only for the author's unmatched insight, but also for the fun and the challenge of his work. He illustrates many of the most significant and most beautiful combinatorial algorithms that are presently known and provides sample programs that can lead to hours of amusement. In showing how the Stanford GraphBase can generate an almost inexhaustible supply of challenging problems, some of which may lead to the discovery of new and improved algorithms, Knuth proposes friendly competitions. His own initial entries into such competitions are included in the book, and readers are challenged to do better. Features Includes new contributions to our understanding of important algorithms and data structures Provides a standard tool for evaluating combinatorial algorithms Demonstrates a more readable, more practical style of programming Challenges readers to surpass his own efficient algorithms 0201542757B04062001

The Structure of Complex Networks
The Structure of Complex Networks

Author: Ernesto Estrada

Publisher: Oxford University Press

Published: 2012

Total Pages: 478

ISBN-13: 019959175X

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The book integrates approaches from mathematics, physics and computer sciences to analyse the organisation of complex networks. Every organisational principle of networks is defined, quantified and then analysed for its influences on the properties and functions of molecular, biological, ecological and social networks.

Principles of Social Networking
Principles of Social Networking

Author: Anupam Biswas

Publisher: Springer Nature

Published: 2021-08-18

Total Pages: 447

ISBN-13: 9811633983

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This book presents new and innovative current discoveries in social networking which contribute enough knowledge to the research community. The book includes chapters presenting research advances in social network analysis and issues emerged with diverse social media data. The book also presents applications of the theoretical algorithms and network models to analyze real-world large-scale social networks and the data emanating from them as well as characterize the topology and behavior of these networks. Furthermore, the book covers extremely debated topics, surveys, future trends, issues, and challenges.

Spectral Algorithms
Spectral Algorithms

Author: Ravindran Kannan

Publisher: Now Publishers Inc

Published: 2009

Total Pages: 153

ISBN-13: 1601982747

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Spectral methods refer to the use of eigenvalues, eigenvectors, singular values and singular vectors. They are widely used in Engineering, Applied Mathematics and Statistics. More recently, spectral methods have found numerous applications in Computer Science to "discrete" as well as "continuous" problems. Spectral Algorithms describes modern applications of spectral methods, and novel algorithms for estimating spectral parameters. The first part of the book presents applications of spectral methods to problems from a variety of topics including combinatorial optimization, learning and clustering. The second part of the book is motivated by efficiency considerations. A feature of many modern applications is the massive amount of input data. While sophisticated algorithms for matrix computations have been developed over a century, a more recent development is algorithms based on "sampling on the fly" from massive matrices. Good estimates of singular values and low rank approximations of the whole matrix can be provably derived from a sample. The main emphasis in the second part of the book is to present these sampling methods with rigorous error bounds. It also presents recent extensions of spectral methods from matrices to tensors and their applications to some combinatorial optimization problems.