000			a
999			_c33055 _d33055
008			240319b xxu||||| |||| 00| 0 eng d
020			_a9783031007835
082			_a006.312 _bKOU
100			_aKoutra, Danai
245			_aIndividual and collective graph mining : principles, algorithms, and applications
260			_bSpringer, _c2018 _aCham :
300			_axi, 194 p. ; _bill., (some col.), _c24 cm
365			_b59.99 _c€ _d93.50
490			_aSynthesis Lectures on Data Mining and Knowledge Discovery ; _vv14
504			_aIncludes bibliographical references.
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 and Collective Graph Mining.
650			_aData Mining
650			_aKnowledge Discovery Statistics
650			_aGraph Mining
700			_aFaloutsos, Christos
942			_2ddc _cBK