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008			221112s2022 sz | s |0|| 0|eng d
020			_a9783030975685
024	7		_a10.1007/978-3-030-97568-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA75.5-76.95
100	1		_aFang, Yixiang _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685321
245	1	0	_aCohesive Subgraph Search Over Large Heterogeneous Information Networks _cby Yixiang Fang, Kai Wang, Xuemin Lin, Wenjie Zhang
250			_aFirst edition 2022
264		1	_aCham _bSpringer International Publising _c2022
300			_a1 recurso en línea (XIX, 74 páginas) _b20 ilustraciones, 5 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSpringerBriefs in Computer Science _x2191-5776
505	0		_aIntroduction -- Preliminaries -- CSS on Bipartite Networks -- CSS on Other General HINs -- Comparison Analysis -- Related Work on CSMs and solutions -- Future Work and Conclusion.
520			_aThis SpringerBrief provides the first systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs). It also covers the research breakthroughs of this area, including models, algorithms and comparison studies in recent years. This SpringerBrief offers a list of promising future research directions of performing CSS over large HINs. The authors first classify the existing works of CSS over HINs according to the classic cohesiveness metrics such as core, truss, clique, connectivity, density, etc., and then extensively review the specific models and their corresponding search solutions in each group. Note that since the bipartite network is a special case of HINs, all the models developed for general HINs can be directly applied to bipartite networks, but the models customized for bipartite networks may not be easily extended for other general HINs due to their restricted settings. The authors also analyze and compare these cohesive subgraph models (CSMs) and solutions systematically. Specifically, the authors compare different groups of CSMs and analyze both their similarities and differences, from multiple perspectives such as cohesiveness constraints, shared properties, and computational efficiency. Then, for the CSMs in each group, the authors further analyze and compare their model properties and high-level algorithm ideas. This SpringerBrief targets researchers, professors, engineers and graduate students, who are working in the areas of graph data management and graph mining. Undergraduate students who are majoring in computer science, databases, data and knowledge engineering, and data science will also want to read this SpringerBrief.
988			_aSpringer_Computer_2022
650		7	_2embne _9162648 _aData mining _vCongresos y asambleas
650		7	_2embne _9156434 _aProceso distribuido (Informática) _vCongresos y asambleas
700	1		_aWang, Kai _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9671124
700	1		_aLin, Xuemin _eautor _0(orcid)0000-0003-2396-7225 _1https://orcid.org/0000-0003-2396-7225 _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685322
700	1		_aZhang, Wenjie _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9685323 _c(Computer scientist)
776	0	8	_iPrinted edition: _z9783030975678
776	0	8	_iPrinted edition: _z9783030975692
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-97568-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2022 _dz _eIG _zSI