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Sprache: Englisch
Verlag: Cambridge University Press, 2004
ISBN 10: 0521840856 ISBN 13: 9780521840859
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Sprache: Englisch
Verlag: Cambridge University Press, 2004
ISBN 10: 0521840856 ISBN 13: 9780521840859
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Zustand: New. A comprehensive, sweeping work by an acclaimed team of authors at the forefront of this hot topic, Understanding Large Temporal Networks and Spatial Networks explores the different approaches to studying large temporal and spatial networks and links them to computationally sound models of changing structure to detect patterns. Series: Wiley Series in Computational and Quantitative Social Science. Num Pages: 464 pages. BIC Classification: JHB; UYQ. Category: (P) Professional & Vocational. Dimension: 251 x 171 x 26. Weight in Grams: 1040. . 2014. Hardback. . . . . Books ship from the US and Ireland.
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In den WarenkorbHardcover. Zustand: Brand New. 460 pages. 10.00x7.00x1.00 inches. In Stock.
Sprache: Englisch
Verlag: Cambridge University Press, 2005
ISBN 10: 0521840856 ISBN 13: 9780521840859
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In den WarenkorbZustand: New. Vladimir BatageljDepartment of Mathematics, Faculty of Mathematics and Physics, University of Ljubljana, SloveniaPatrick DoreianFaculty of Social Sciences, University of Ljubljana, Slovenia and Department of Sociology, University of Pittsburgh, USAAnuska Fe.
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Zustand: New. Patrick Doreian, MA, is Professor Emeritus of Sociology and Statistics at the University of Pittsburgh and has a research position at the Faculty of Social Sciences at the University of Ljubljana. He has published over 150 articles in academic journals as w.
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Buch. Zustand: Neu. Advances in Network Clustering and Blockmodeling | Anuska Ferligoj (u. a.) | Buch | Wiley Series in Computational and Quantitative Social Science | 432 S. | Englisch | 2020 | John Wiley & Sons Inc | EAN 9781119224709 | Verantwortliche Person für die EU: Wiley-VCH GmbH, Boschstr. 12, 69469 Weinheim, product-safety[at]wiley[dot]com | Anbieter: preigu.
Buch. Zustand: Neu. Neuware - Provides an overview of the developments and advances in the field of network clustering and blockmodeling over the last 10 yearsThis book offers an integrated treatment of network clustering and blockmodeling, covering all of the newest approaches and methods that have been developed over the last decade. Presented in a comprehensive manner, it offers the foundations for understanding network structures and processes, and features a wide variety of new techniques addressing issues that occur during the partitioning of networks across multiple disciplines such as community detection, blockmodeling of valued networks, role assignment, and stochastic blockmodeling.Written by a team of international experts in the field, Advances in Network Clustering and Blockmodeling offers a plethora of diverse perspectives covering topics such as: bibliometric analyses of the network clustering literature; clustering approaches to networks; label propagation for clustering; and treating missing network data before partitioning. It also examines the partitioning of signed networks, multimode networks, and linked networks. A chapter on structured networks and coarsegrained descriptions is presented, along with another on scientific coauthorship networks. The book finishes with a section covering conclusions and directions for future work. In addition, the editors provide numerous tables, figures, case studies, examples, datasets, and more.\* Offers a clear and insightful look at the state of the art in network clustering and blockmodeling\* Provides an excellent mix of mathematical rigor and practical application in a comprehensive manner\* Presents a suite of new methods, procedures, algorithms for partitioning networks, as well as new techniques for visualizing matrix arrays\* Features numerous examples throughout, enabling readers to gain a better understanding of research methods and to conduct their own research effectively\* Written by leading contributors in the field of spatial networks analysisAdvances in Network Clustering and Blockmodeling is an ideal book for graduate and undergraduate students taking courses on network analysis or working with networks using real data. It will also benefit researchers and practitioners interested in network analysis.