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001 978-3-031-01911-1
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008 220601s2018 sz | s |||| 0|eng d
020 _a9783031019111
_9978-3-031-01911-1
024 7 _a10.1007/978-3-031-01911-1
_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 _aKoutra, Danai.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980426
245 1 0 _aIndividual and Collective Graph Mining
_h[electronic resource] :
_bPrinciples, Algorithms, and Applications /
_cby Danai Koutra, Christos Faloutsos.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXI, 197 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 _aAcknowledgments -- Introduction -- Summarization of Static Graphs -- Inference in a Graph -- Summarization of Dynamic Graphs -- Graph Similarity -- Graph Alignment -- Conclusions and Further Research Problems -- Bibliography -- Authors' Biographies .
520 _aGraphs 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: We show how to interpretably summarize a single graph by identifying its important graph structures. We complement summarization with inference, which leverages information about few entities (obtained via summarization or other methods) and the network structure to efficiently and effectively learn information about the unknown entities. Collective Graph Mining: We extend the idea of individual-graph summarization to time-evolving graphs, and show how to scalably discover temporal patterns. Apart from summarization, we claim that graph similarity is often the underlying problem in a host of applications where multiple graphs occur (e.g., temporal anomaly detection, discovery of behavioral patterns), and we present principled, scalable algorithms for aligning networks and measuring their similarity. The methods that we present in this book leverage techniques from diverse areas, such as matrix algebra, graph theory, optimization, information theory, machine learning, finance, and social science,to solve real-world problems. We present applications of our exploration algorithms to massive datasets, including a Web graph of 6.6 billion edges, a Twitter graph of 1.8 billion edges, brain graphs with up to 90 million edges, collaboration, peer-to-peer networks, browser logs, all spanning millions of users and interactions.
650 0 _aData mining.
_93907
650 0 _aStatistics .
_931616
650 1 4 _aData Mining and Knowledge Discovery.
_980427
650 2 4 _aStatistics.
_914134
700 1 _aFaloutsos, Christos.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980428
710 2 _aSpringerLink (Online service)
_980429
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031001062
776 0 8 _iPrinted edition:
_z9783031007835
776 0 8 _iPrinted edition:
_z9783031030390
830 0 _aSynthesis Lectures on Data Mining and Knowledge Discovery,
_x2151-0075
_980430
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01911-1
912 _aZDB-2-SXSC
942 _cEBK
999 _c84959
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