000 04312nam a22005175i 4500
001 978-3-031-01903-6
003 DE-He213
005 20240730163746.0
007 cr nn 008mamaa
008 220601s2012 sz | s |||| 0|eng d
020 _a9783031019036
_9978-3-031-01903-6
024 7 _a10.1007/978-3-031-01903-6
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema
082 0 4 _a006.312
_223
100 1 _aChakrabarti, Deepayan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980417
245 1 0 _aGraph Mining
_h[electronic resource] :
_bLaws, Tools, and Case Studies /
_cby Deepayan Chakrabarti, Christos Faloutsos.
250 _a1st ed. 2012.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2012.
300 _aXVI, 191 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aData mining.
_93907
650 0 _aStatistics .
_931616
650 1 4 _aData Mining and Knowledge Discovery.
_980418
650 2 4 _aStatistics.
_914134
700 1 _aFaloutsos, Christos.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980419
710 2 _aSpringerLink (Online service)
_980420
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031007750
776 0 8 _iPrinted edition:
_z9783031030314
830 0 _aSynthesis Lectures on Data Mining and Knowledge Discovery,
_x2151-0075
_980421
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01903-6
912 _aZDB-2-SXSC
942 _cEBK
999 _c84957
_d84957