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Algorithms And Models For Network Data And Link Analysis

Algorithms and Models for Network Data and Link Analysis PDF

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Author: François Fouss
Publisher: Cambridge University Press
ISBN: 1316712516
Size: 54.12 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages :
View: 1862


Network data are produced automatically by everyday interactions - social networks, power grids, and links between data sets are a few examples. Such data capture social and economic behavior in a form that can be analyzed using powerful computational tools. This book is a guide to both basic and advanced techniques and algorithms for extracting useful information from network data. The content is organized around 'tasks', grouping the algorithms needed to gather specific types of information and thus answer specific types of questions. Examples include similarity between nodes in a network, prestige or centrality of individual nodes, and dense regions or communities in a network. Algorithms are derived in detail and summarized in pseudo-code. The book is intended primarily for computer scientists, engineers, statisticians and physicists, but it is also accessible to network scientists based in the social sciences. MATLAB®/Octave code illustrating some of the algorithms will be available at: http://www.cambridge.org/9781107125773.

Statistical Analysis Of Network Data

Statistical Analysis of Network Data PDF

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Author: Eric D. Kolaczyk
Publisher: Springer Science & Business Media
ISBN: 0387881468
Size: 12.48 MB
Format: PDF
Category : Computers
Languages : en
Pages : 386
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In recent years there has been an explosion of network data – that is, measu- ments that are either of or from a system conceptualized as a network – from se- ingly all corners of science. The combination of an increasingly pervasive interest in scienti c analysis at a systems level and the ever-growing capabilities for hi- throughput data collection in various elds has fueled this trend. Researchers from biology and bioinformatics to physics, from computer science to the information sciences, and from economics to sociology are more and more engaged in the c- lection and statistical analysis of data from a network-centric perspective. Accordingly, the contributions to statistical methods and modeling in this area have come from a similarly broad spectrum of areas, often independently of each other. Many books already have been written addressing network data and network problems in speci c individual disciplines. However, there is at present no single book that provides a modern treatment of a core body of knowledge for statistical analysis of network data that cuts across the various disciplines and is organized rather according to a statistical taxonomy of tasks and techniques. This book seeks to ll that gap and, as such, it aims to contribute to a growing trend in recent years to facilitate the exchange of knowledge across the pre-existing boundaries between those disciplines that play a role in what is coming to be called ‘network science.

Link Mining Models Algorithms And Applications

Link Mining  Models  Algorithms  and Applications PDF

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Author: Philip S. Yu
Publisher: Springer Science & Business Media
ISBN: 9781441965158
Size: 29.59 MB
Format: PDF, ePub, Mobi
Category : Science
Languages : en
Pages : 586
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This book offers detailed surveys and systematic discussion of models, algorithms and applications for link mining, focusing on theory and technique, and related applications: text mining, social network analysis, collaborative filtering and bioinformatics.

Scalable Algorithms For Data And Network Analysis

Scalable Algorithms for Data and Network Analysis PDF

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Author: Shang-Hua Teng
Publisher:
ISBN: 9781680831306
Size: 57.20 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 292
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In the age of Big Data, efficient algorithms are in high demand. It is also essential that efficient algorithms should be scalable. This book surveys a family of algorithmic techniques for the design of scalable algorithms. These techniques include local network exploration, advanced sampling, sparsification, and geometric partitioning.

Data Streams

Data Streams PDF

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Author: S. Muthukrishnan
Publisher: Now Publishers Inc
ISBN: 9781933019147
Size: 44.32 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 126
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In the data stream scenario, input arrives very rapidly and there is limited memory to store the input. Algorithms have to work with one or few passes over the data, space less than linear in the input size or time significantly less than the input size. In the past few years, a new theory has emerged for reasoning about algorithms that work within these constraints on space, time, and number of passes. Some of the methods rely on metric embeddings, pseudo-random computations, sparse approximation theory and communication complexity. The applications for this scenario include IP network traffic analysis, mining text message streams and processing massive data sets in general. Researchers in Theoretical Computer Science, Databases, IP Networking and Computer Systems are working on the data stream challenges

Data Analytics

Data Analytics PDF

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Author: Thomas A. Runkler
Publisher: Springer Science & Business Media
ISBN: 3834825883
Size: 29.23 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 137
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This book is a comprehensive introduction to the methods and algorithms and approaches of modern data analytics. It covers data preprocessing, visualization, correlation, regression, forecasting, classification, and clustering. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. The text is designed for undergraduate and graduate courses on data analytics for engineering, computer science, and math students. It is also suitable for practitioners working on data analytics projects. This book has been used for more than ten years in numerous courses at the Technical University of Munich, Germany, in short courses at several other universities, and in tutorials at scientific conferences. Much of the content is based on the results of industrial research and development projects at Siemens.

Mathematics Of Neural Networks

Mathematics of Neural Networks PDF

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Author: Stephen W. Ellacott
Publisher: Springer Science & Business Media
ISBN: 9780792399339
Size: 35.49 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 403
View: 1473


This volume of research papers comprises the proceedings of the first International Conference on Mathematics of Neural Networks and Applications (MANNA), which was held at Lady Margaret Hall, Oxford from July 3rd to 7th, 1995 and attended by 116 people. The meeting was strongly supported and, in addition to a stimulating academic programme, it featured a delightful venue, excellent food and accommo dation, a full social programme and fine weather - all of which made for a very enjoyable week. This was the first meeting with this title and it was run under the auspices of the Universities of Huddersfield and Brighton, with sponsorship from the US Air Force (European Office of Aerospace Research and Development) and the London Math ematical Society. This enabled a very interesting and wide-ranging conference pro gramme to be offered. We sincerely thank all these organisations, USAF-EOARD, LMS, and Universities of Huddersfield and Brighton for their invaluable support. The conference organisers were John Mason (Huddersfield) and Steve Ellacott (Brighton), supported by a programme committee consisting of Nigel Allinson (UMIST), Norman Biggs (London School of Economics), Chris Bishop (Aston), David Lowe (Aston), Patrick Parks (Oxford), John Taylor (King's College, Lon don) and Kevin Warwick (Reading). The local organiser from Huddersfield was Ros Hawkins, who took responsibility for much of the administration with great efficiency and energy. The Lady Margaret Hall organisation was led by their bursar, Jeanette Griffiths, who ensured that the week was very smoothly run.

Data Mining And Analysis

Data Mining and Analysis PDF

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Author: Mohammed J. Zaki
Publisher: Cambridge University Press
ISBN: 0521766338
Size: 77.96 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 562
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A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.

Security Of Information And Networks

Security of Information and Networks PDF

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Author: Atilla Elçi
Publisher: Trafford Publishing
ISBN: 1425141099
Size: 36.89 MB
Format: PDF
Category : Computers
Languages : en
Pages : 369
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Security of Information and Networks includes invited and contributed papers on information assurance, security, and public policy. It covers Ciphers, Mobile Agents, Access Control, Security Assurance, Intrusion Detection, and Security Software.

Statistical Analysis Of Network Data With R

Statistical Analysis of Network Data with R PDF

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Author: Eric D. Kolaczyk
Publisher: Springer
ISBN: 1493909835
Size: 25.91 MB
Format: PDF
Category : Computers
Languages : en
Pages : 207
View: 5424


Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).

Computational Network Analysis With R

Computational Network Analysis with R PDF

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Author: Matthias Dehmer
Publisher: John Wiley & Sons
ISBN: 3527339582
Size: 78.32 MB
Format: PDF, Mobi
Category : Medical
Languages : en
Pages : 368
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This new title in the well-established "Quantitative Network Biology" series includes innovative and existing methods for analyzing network data in such areas as network biology and chemoinformatics. With its easy-to-follow introduction to the theoretical background and application-oriented chapters, the book demonstrates that R is a powerful language for statistically analyzing networks and for solving such large-scale phenomena as network sampling and bootstrapping. Written by editors and authors with an excellent track record in the field, this is the ultimate reference for R in Network Analysis.

Social Network Data Analytics

Social Network Data Analytics PDF

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Author: Charu C. Aggarwal
Publisher: Springer Science & Business Media
ISBN: 1441984623
Size: 67.11 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 502
View: 6727


Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

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