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Graph Mining: Laws, Tools, and Case Studies (Synthesis Lectures on Data Mining and Knowledge Discovery)
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Graph Mining : Laws, Tools, and Case Studies
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Graph Mining : Laws, Tools, and Case Studies
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - What does the Web look like How can we find patterns, communities, outliers, in a social network Which are the most central nodes in a network These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for example in social networks, computer-communication networks (intrusion detection, traffic management), protein-protein interaction networks in biology, document-text bipartite graphs in text retrieval, person-account graphs in financial fraud detection, and others. In this work, first we list several surprising patterns that real graphs tend to follow. Then we give a detailed list of generators that try to mirror these patterns. Generators are important, because they can help with 'what if' scenarios, extrapolations, and anonymization. Then we provide a list of powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition (SVD)), tensors, and case studies like the famous 'pageRank' algorithm and the 'HITS' algorithm for ranking web search results. Finally, we conclude with a survey of tools and observations from related fields like sociology, which provide complementary viewpoints. Table of Contents: Introduction / Patterns in Static Graphs / Patterns in Evolving Graphs / Patterns in Weighted Graphs / Discussion: The Structure of Specific Graphs / Discussion: Power Laws and Deviations / Summary of Patterns / Graph Generators / Preferential Attachment and Variants / Incorporating Geographical Information / The RMat / Graph Generation by Kronecker Multiplication / Summary and Practitioner's Guide / SVD, Random Walks, and Tensors / Tensors / Community Detection / Influence/Virus Propagation and Immunization / Case Studies / Social Networks / Other Related Work / Conclusions.
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Taschenbuch. Condition: Neu. Graph Mining | Laws, Tools, and Case Studies | Deepayan Chakrabarti (u. a.) | Taschenbuch | Synthesis Lectures on Data Mining and Knowledge Discovery | xvi | Englisch | 2012 | Springer | EAN 9783031007750 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Graph Mining: Laws, Tools, and Case Studies (Synthesis Lectures on Data Mining and Knowledge Discovery)
Language: English
Published by Morgan & Claypool Publishers, 2012
ISBN 10: 1608451151 ISBN 13: 9781608451159
Condition: good. Fast Free Shipping â" Good condition. It may show normal signs of use, such as light writing, highlighting, or library markings, but all pages are intact and the book is fully readable. A solid, complete copy that's ready to enjoy.
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Graph Mining
Language: English
Published by Springer, Berlin|Springer International Publishing|Morgan & Claypool|Springer, 2012
ISBN 10: 3031007751 ISBN 13: 9783031007750
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. What does the Web look like? How can we find patterns, communities, outliers, in a social network? Which are the most central nodes in a network? These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for examp.
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Graph Mining
Language: English
Published by Springer, Springer Okt 2012, 2012
ISBN 10: 3031007751 ISBN 13: 9783031007750
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -What does the Web look like How can we find patterns, communities, outliers, in a social network Which are the most central nodes in a network These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for example in social networks, computer-communication networks (intrusion detection, traffic management), protein-protein interaction networks in biology, document-text bipartite graphs in text retrieval, person-account graphs in financial fraud detection, and others. In this work, first we list several surprising patterns that real graphs tend to follow. Then we give a detailed list of generators that try to mirror these patterns. Generators are important, because they can help with 'what if' scenarios, extrapolations, and anonymization. Then we provide a list of powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition (SVD)), tensors, and case studies like the famous 'pageRank' algorithm and the 'HITS' algorithm for ranking web search results. Finally, we conclude with a survey of tools and observations from related fields like sociology, which provide complementary viewpoints. Table of Contents: Introduction / Patterns in Static Graphs / Patterns in Evolving Graphs / Patterns in Weighted Graphs / Discussion: The Structure of Specific Graphs / Discussion: Power Laws and Deviations / Summary of Patterns / Graph Generators / Preferential Attachment and Variants / Incorporating Geographical Information / The RMat / Graph Generation by Kronecker Multiplication / Summary and Practitioner's Guide / SVD, Random Walks, and Tensors / Tensors / Community Detection / Influence/Virus Propagation and Immunization / Case Studies / Social Networks / Other Related Work / ConclusionsSpringer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 208 pp. Englisch.
