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008			220601s2012 sz | s |||| 0|eng d
020			_a9783031019036
024	7		_a10.1007/978-3-031-01903-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aHM741 _b2012 EB
100	1		_aChakrabarti, Deepayan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687225
245	1	0	_aGraph Mining : _bLaws, Tools, and Case Studies _cby Deepayan Chakrabarti, Christos Faloutsos
250			_a1st edition 2012
264		1	_aCham _bSpringer International Publishing _c2012
300			_a1 recurso en línea (XVI, 191 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Data Mining and Knowledge Discovery _x2151-0075
505	0		_aIntroduction -- 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.
520			_aWhat 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.
988			_aSynthesis Collection of Technology_2012
650		7	_2embne _9141354 _aRedes informáticas _xModelos matemáticos
650		7	_2embne _9162648 _aData mining
650		7	_2embne _9431622 _aRedes sociales en Internet _xModelos matemáticos
700	1		_aFaloutsos, Christos _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687226
776	0	8	_iPrinted edition: _z9783031007750
776	0	8	_iPrinted edition: _z9783031030314
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01903-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI