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008			220601s2018 sz | s |||| 0|eng d
020			_a9783031019111
024	7		_a10.1007/978-3-031-01911-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.9 _b2018 EB
100	1		_aKoutra, Danai _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687231
245	1	0	_aIndividual and Collective Graph Mining : _bPrinciples, Algorithms, and Applications _cby Danai Koutra, Christos Faloutsos
250			_a1st edition 2018
264		1	_aCham _bSpringer International Publishing _c2018
300			_a1 recurso en línea (XI, 197 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		_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.
988			_aSynthesis Collection of Technology_2018
650		7	_2embne _9162648 _aData mining
650		7	_2embne _9146336 _aGrafos, Teoría de _xProceso de datos
650		7	_2embne _9687232 _aMétodos gráficos _xProceso de datos
700	1		_aFaloutsos, Christos _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687226
776	0	8	_iPrinted edition: _z9783031001062
776	0	8	_iPrinted edition: _z9783031007835
776	0	8	_iPrinted edition: _z9783031030390
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01911-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI