大葉大學圖書館 |

Individual and collective graph mining :principles, algorithms, and applications /

レコード種別:	コンピュータ・メディア : 単行資料
[NT 15000414] null:	006.3
タイトル / 著者:	Individual and collective graph mining : : principles, algorithms, and applications // Danai Koutra, Christos Faloutsos.
著者:	Koutra, Danai,
その他の著者:	Faloutsos, Christos,
出版された:	[San Rafael, California] : : Morgan & Claypool,, 2018.
記述:	1 PDF (xi, 194 pages) : : illustrations.
注記:	Part of: Synthesis digital library of engineering and computer science.
主題:	Data mining.
主題:	Graph theory - Data processing.
主題:	Graphic methods - Data processing.
国際標準図書番号 (ISBN) :	9781681730400
[NT 15000227] null:	Includes bibliographical references (pages 171-192).
[NT 15000228] null:	1. Introduction -- 1.1 Overview -- 1.2 Organization of this book -- 1.2.1 Part I: Individual graph mining -- 1.2.2 Part II: Collective graph mining -- 1.2.3 Code and supporting materials on the web -- 1.3 Preliminaries -- 1.3.1 Graph definitions -- 1.3.2 Graph-theoretic data structures -- 1.3.3 Linear algebra concepts -- 1.3.4 Select graph properties -- 1.4 Common symbols --
[NT 15000229] null:	Graphs naturally represent information ranging from links between web pages, to communication in email networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect anomalies that indicate critical events, such as an attack on a computer system, disease formation in the human brain, or the fall of a company? This book presents scalable, principled discovery algorithms that combine globality with locality to make sense of one or more graphs. In addition to fast algorithmic methodologies, we also contribute graph-theoretical ideas and models, and real-world applications in two main areas : Individual Graph Mining and Collective Graph Mining.
電子資源:	http://ieeexplore.ieee.org/servlet/opac?bknumber=8094360

Individual and collective graph mining :principles, algorithms, and applications /
Koutra, Danai,

Individual and collective graph mining :principles, algorithms, and applications /Danai Koutra, Christos Faloutsos. - [San Rafael, California] :Morgan & Claypool,2018. - 1 PDF (xi, 194 pages) :illustrations. - Synthesis lectures on data mining and knowledge discovery,# 142151-0075 ;. - Synthesis digital library of engineering and computer science..

Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 171-192).

1. Introduction -- 1.1 Overview -- 1.2 Organization of this book -- 1.2.1 Part I: Individual graph mining -- 1.2.2 Part II: Collective graph mining -- 1.2.3 Code and supporting materials on the web -- 1.3 Preliminaries -- 1.3.1 Graph definitions -- 1.3.2 Graph-theoretic data structures -- 1.3.3 Linear algebra concepts -- 1.3.4 Select graph properties -- 1.4 Common symbols --

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Compendex

Graphs naturally represent information ranging from links between web pages, to communication in email networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect anomalies that indicate critical events, such as an attack on a computer system, disease formation in the human brain, or the fall of a company? This book presents scalable, principled discovery algorithms that combine globality with locality to make sense of one or more graphs. In addition to fast algorithmic methodologies, we also contribute graph-theoretical ideas and models, and real-world applications in two main areas : Individual Graph Mining and Collective Graph Mining.

Mode of access: World Wide Web.

ISBN: 9781681730400

Standard No.: 10.2200/S00796ED1V01Y201708DMK014doiSubjects--Topical Terms:

337740
Data mining.
Subjects--Index Terms:

data miningIndex Terms--Genre/Form:

336502
Electronic books.

LC Class. No.: QA76.9 / .K685 2018

Dewey Class. No.: 006.3

Individual and collective graph mining :principles, algorithms, and applications /
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245 1 0 $a Individual and collective graph mining : $b principles, algorithms, and applications / $c Danai Koutra, Christos Faloutsos.
260 1 $a [San Rafael, California] : $b Morgan & Claypool, $c 2018.
264 1 $a [San Rafael, California] : $b Morgan & Claypool, $c 2018.
300 $a 1 PDF (xi, 194 pages) : $b illustrations.
336 $a text $2 rdacontent
337 $a electronic $2 isbdmedia
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490 1 $a Synthesis lectures on data mining and knowledge discovery, $x 2151-0075 ; $v # 14
500 $a Part of: Synthesis digital library of engineering and computer science.
504 $a Includes bibliographical references (pages 171-192).
505 0 $a 1. Introduction -- 1.1 Overview -- 1.2 Organization of this book -- 1.2.1 Part I: Individual graph mining -- 1.2.2 Part II: Collective graph mining -- 1.2.3 Code and supporting materials on the web -- 1.3 Preliminaries -- 1.3.1 Graph definitions -- 1.3.2 Graph-theoretic data structures -- 1.3.3 Linear algebra concepts -- 1.3.4 Select graph properties -- 1.4 Common symbols --
505 8 $a Part I. Individual graph mining -- 2. Summarization of static graphs -- 2.1 Overview and motivation -- 2.2 Problem formulation -- 2.2.1 MDL for graph summarization -- 2.2.2 Encoding the model -- 2.2.3 Encoding the errors -- 2.3 VoG: vocabulary-based summarization of graphs -- 2.3.1 Subgraph generation -- 2.3.2 Subgraph labeling -- 2.3.3 Summary assembly -- 2.3.4 Toy example -- 2.3.5 Time complexity -- 2.4 Empirical results -- 2.4.1 Quantitative analysis -- 2.4.2 Qualitative analysis -- 2.4.3 Scalability -- 2.5 Discussion -- 2.6 Related work -- 3. Inference in a graph -- 3.1 Guilt-by-association techniques -- 3.1.1 Random walk with restarts (RWR) -- 3.1.2 Semi-supervised learning (SSL) -- 3.1.3 Belief propagation (BP) -- 3.1.4 Summary -- 3.2 FaBP: fast belief propagation -- 3.2.1 Derivation -- 3.2.2 Analysis of convergence -- 3.2.3 Algorithm -- 3.3 Extension to multiple classes -- 3.4 Empirical results -- 3.4.1 Accuracy -- 3.4.2 Convergence -- 3.4.3 Robustness -- 3.4.4 Scalability --
505 8 $a Part II. Collective graph mining -- 4. Summarization of dynamic graphs -- 4.1 Problem formulation -- 4.1.1 MDL for dynamic graph summarization -- 4.1.2 Encoding the model -- 4.1.3 Encoding the errors -- 4.2 TimeCrunch: vocabulary-based summarization of dynamic graphs -- 4.2.1 Generating candidate static structures -- 4.2.2 Labeling candidate static structures -- 4.2.3 Stitching candidate temporal structures -- 4.2.4 Composing the summary -- 4.3 Empirical results -- 4.3.1 Quantitative analysis -- 4.3.2 Qualitative analysis -- 4.3.3 Scalability -- 4.4 Related work -- 5. Graph similarity -- 5.1 Intuition -- 5.1.1 Overview -- 5.1.2 Measuring node affinities -- 5.1.3 Leveraging belief propagation -- 5.1.4 Desired properties for similarity measures -- 5.2 DeltaCon: "[theta]" connectivity change detection -- 5.2.1 Algorithm description -- 5.2.2 Faster computation -- 5.2.3 Desired properties -- 5.3 DeltaCon-ATTR: adding node and edge attribution -- 5.3.1 Algorithm description -- 5.3.2 Scalability analysis -- 5.4 Empirical results -- 5.4.1 Intuitiveness of DeltaCon -- 5.4.2 Intuitiveness of DeltaCon-ATTR -- 5.4.3 Scalability -- 5.4.4 Robustness -- 5.5 Applications -- 5.5.1 Enron -- 5.5.2 Brain connectivity graph clustering -- 5.5.3 Recovery of connectome correspondences -- 5.6 Related work -- 6. Graph alignment -- 6.1 Problem formulation -- 6.2 BiG-align: bipartite graph alignment -- 6.2.1 Mathematical formulation -- 6.2.2 Problem-specific optimizations -- 6.2.3 Algorithm description -- 6.3 Uni-align: extension to unipartite graph alignment -- 6.4 Empirical results -- 6.4.1 Accuracy and runtime of BiG-align -- 6.4.2 Accuracy and runtime of INI-align -- 6.5 Discussion -- 6.6 Related work -- 7. Conclusions and further research problems -- Bibliography -- Authors' biographies.
506 $a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 $a Compendex
510 0 $a INSPEC
510 0 $a Google scholar
510 0 $a Google book search
520 3 $a Graphs naturally represent information ranging from links between web pages, to communication in email networks, to connections between neurons in our brains. These graphs often span billions of nodes and interactions between them. Within this deluge of interconnected data, how can we find the most important structures and summarize them? How can we efficiently visualize them? How can we detect anomalies that indicate critical events, such as an attack on a computer system, disease formation in the human brain, or the fall of a company? This book presents scalable, principled discovery algorithms that combine globality with locality to make sense of one or more graphs. In addition to fast algorithmic methodologies, we also contribute graph-theoretical ideas and models, and real-world applications in two main areas : Individual Graph Mining and Collective Graph Mining.
530 $a Also available in print.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Title from PDF title page (viewed on October 25, 2017).
650 0 $a Data mining. $3 337740
650 0 $a Graph theory $x Data processing. $3 561823
650 0 $a Graphic methods $x Data processing. $3 729006
653 $a data mining
653 $a graph mining and exploration
653 $a graph similarity
653 $a graph matching
653 $a network alignment
653 $a graph summarization
653 $a pattern mining
653 $a outlier detection
653 $a anomaly detection
653 $a scalability
653 $a fast algorithms
653 $a models
653 $a visualization
653 $a social networks
653 $a brain graphs
653 $a connectomes
655 0 $a Electronic books. $2 local $3 336502
700 1 $a Faloutsos, Christos, $e author. $3 729034
776 0 8 $i Print version: $z 9781681730394
830 0 $a Synthesis digital library of engineering and computer science. $3 461208
830 0 $a Synthesis lectures on data mining and knowledge discovery ; $v # 15. $x 2151-0075 $3 728970
856 4 2 $3 Abstract with links to resource $u http://ieeexplore.ieee.org/servlet/opac?bknumber=8094360