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Statistical Analysis of Network Data de Eric D. Kolaczyk

Statistical Analysis of Network Data

Autor Eric D. Kolaczyk
en Limba Engleză Paperback – 6 dec 2010

Diferența majoră dintre această lucrare și documentația tehnică fragmentată rezidă în capacitatea autorului Eric D. Kolaczyk de a unifica metodele statistice sub o singură umbrelă teoretică. Putem afirma că, deși există numeroase resurse axate pe discipline specifice, Statistical Analysis of Network Data reușește să umple un gol critic prin oferirea unei taxonomii moderne, aplicabile oricărui sistem conceptualizat ca rețea. Notăm cu interes modul în care autorul abordează complexitatea datelor de tip high-throughput, transformând observațiile brute în modele matematice riguroase.

Structura volumului reflectă o progresie logică și tehnică: pornim de la fundamente și maparea rețelelor, trecem prin analiza descriptivă a caracteristicilor grafurilor și ajungem la subiecte avansate precum inferența topologiei și analiza fluxului de date. Dacă Networks de Mark Newman v-a oferit cadrul teoretic extins asupra rețelelor complexe, această carte oferă instrumentele practice de analiză statistică necesare pentru cercetarea aplicată. Găsim în acest volum o rigoare care lipsește adesea din ghidurile pur computaționale, punând accent pe estimare și modelare predictivă.

În contextul operei autorului, acest titlu reprezintă fundamentul teoretic pentru lucrarea sa ulterioară, Statistical Analysis of Network Data with R. Dacă volumul de față stabilește „ce” și „de ce” în analiza rețelelor, continuarea sa se concentrează pe „cum” prin intermediul limbajului R. De asemenea, lucrarea Topics at the Frontier of Statistics and Network Analysis extinde aceste concepte către zonele de cercetare emergente, însă volumul de față rămâne resursa de referință pentru stăpânirea metodelor de bază în ceea ce numim astăzi „network science”.

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Specificații

ISBN-13: 9781441927767
ISBN-10: 144192776X
Pagini: 400
Ilustrații: XII, 386 p.
Dimensiuni: 155 x 235 x 22 mm
Greutate: 0.6 kg
Ediția:Softcover reprint of hardcover 1st edition 2009
Editura: Springer
Locul publicării:New York, NY, United States

Public țintă

Research

De ce să citești această carte

Recomandăm această carte cercetătorilor și studenților la nivel postuniversitar care au nevoie de o metodologie statistică unitară pentru analiza rețelelor complexe. Cititorul câștigă o înțelegere profundă a modului în care pot fi aplicate modelele matematice pe date provenite din biologie, economie sau sociologie, beneficiind de o structură clară ce acoperă de la eșantionare până la inferență topologică.

Descriere scurtă

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.

Cuprins

and Overview.- Preliminaries.- Mapping Networks.- Descriptive Analysis of Network Graph Characteristics.- Sampling and Estimation in Network Graphs.- Models for Network Graphs.- Network Topology Inference.- Modeling and Prediction for Processes on Network Graphs.- Analysis of Network Flow Data.- Graphical Models.

Recenzii

From the reviews:
“…Accessible and easy to read…strikes a balance between concepts and mathematical detail. …This book is a superb introduction to a fascinating area.” (International Statistical Review, 2010, 78, 1, 134-159) “Many disciplines are nowadays involved in network modeling, but it appears as if a common methodological foundation is lacking. The objective of this book is to provide a first attempt at defining such a common methodological foundation from a statistical point of view. … The style of the writing is excellent. … ample references allow quick access to further literature. I can recommend this book to anyone with a serious statistical interest in networks.” (Fred van Eeuwijk, VOC Nieuwsbrief, Issue 44, May, 2010)
“Any reader interested in networks and wanting a perspective beyond that of any single discipline should acquire this book. … Researchers will also appreciate the many points in the book where important open problems are identified. The book can also serve readily and flexibly as the main textbook for either a graduate-level seminar course or for an informally organized reading group. … This book sets itself the challenge of addressing statistics for network science broadly, and in the many ways already noted, it is successful.” (Michael Frey, Technometrics, Vol. 54 (1), February, 2012)

Textul de pe ultima copertă

In the past decade, the study of networks has increased dramatically. Researchers from across the sciences—including biology and bioinformatics, computer science, economics, engineering, mathematics, physics, sociology, and statistics—are more and more involved with the collection and statistical analysis of network-indexed data. As a result, statistical methods and models are being developed in this area at a furious pace, with contributions coming from a wide spectrum of disciplines.
This book provides an up-to-date treatment of the foundations common to the statistical analysis of network data across the disciplines. The material is organized according to a statistical taxonomy, although the presentation entails a conscious balance of concepts versus mathematics. In addition, the examples—including extended cases studies—are drawn widely from the literature. This book should be of substantial interest both to statisticians and to anyone else working in the area of ‘network science.’
The coverage of topics in this book is broad, but unfolds in a systematic manner, moving from descriptive (or exploratory) methods, to sampling, to modeling and inference. Specific topics include network mapping, characterization of network structure, network sampling, and the modeling, inference, and prediction of networks, network processes, and network flows. This book is the first such resource to present material on all of these core topics in one place.
Eric Kolaczyk is a professor of statistics, and Director of the Program in Statistics, in the Department of Mathematics and Statistics at Boston University, where he also is an affiliated faculty member in the Center for Biodynamics, the Program in Bioinformatics, and the Division of Systems Engineering. His publications on network-based topics include work ranging from the detection of anomalous traffic patterns in computer networks to the prediction of biological function in networks ofinteracting proteins to the characterization of influence of groups of actors in social networks.

Caracteristici

Unified presentation of statistical models and methods from across the variety of disciplines engaged in ‘network science’ Balanced presentation of concepts and mathematics Examples, including extended case studies, drawn widely from applications in the literature Includes supplementary material: sn.pub/extras