000			03200cam a2200349 i 4500
001			37578
003			0000000000
005			20240411192400.0
008			171003s2017 flu s 000 0 eng
010			_a 2017015871
020			_a9781138032439 (hardback : alk. paper)
035			_a20045236
040			_aDLC _beng _cDLC _erda _dDLC
042			_apcc
050	0	0	_aTK5105.88815 _b.S4864 2017
082	0	0	_a302.30285 _223
100	1		_aZheng, Quan, _eauthor.
245	1	0	_aSocial networks with rich edge semantics / _cQuan Zheng, Queen's University Kingston, Ontario, Canada, David Skillicorn Queen's University Kingston, Ontario, Canada.
264		1	_aBoca Raton : _bTaylor & Francis, CRC Press, _c[2017]
300			_axx, 210 pages ; _c24 cm.
336			_atext _2rdacontent
337			_acomputer _2rdamedia
338			_aonline resource _2rdacarrier
490	0		_aChapman & Hall/CRC data mining and knowledge discovery ; _v30
500			_aSocial Networks with Rich Edge Semantics introduces a new mechanism for representing social networks in which pairwise relationships can be drawn from a range of realistic possibilities, including different types of relationships, different strengths in the directions of a pair, positive and negative relationships, and relationships whose intensities change with time. For each possibility, the book shows how to model the social network using spectral embedding. It also shows how to compose the techniques so that multiple edge semantics can be modeled together, and the modeling techniques are then applied to a range of datasets. Features introduces the reader to difficulties with current social network analysis, and the need for richer representations of relationships among nodes, including accounting for intensity, direction, type, positive/negative, and changing intensities over time presents a novel mechanism to allow social networks with qualitatively different kinds of relationships to be described and analyzed includes extensions to the important technique of spectral embedding, shows that they are mathematically well motivated and proves that their results are appropriates hows how to exploit embeddings to understand structures within social networks, including subgroups, positional significance, link or edge prediction, consistency of role in different contexts, and net flow of properties through a node illustrates the use of the approach for real-world problems for online social networks, criminal and drug smuggling networks, and networks where the nodes are themselves groups suitable for researchers and students in social network research, data science, statistical learning, and related areas, this book will help to provide a deeper understanding of real-world social networks.
504			_aIncludes bibliographical references and index.
650		0	_aSemantic Web.
650		0	_aSocial media.
700	1		_aZheng, Quan _c(Telecommunications engineer), _eauthor.
856			_uhttps://drive.google.com/file/d/11XmskSkeg8cDZwep2s7pBh3MyrSHjTzt/view?usp=sharing
999			_c5172 _d5172