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020 _a9783030975685
_9978-3-030-97568-5
024 7 _a10.1007/978-3-030-97568-5
_2doi
050 4 _aQA75.5-76.95
072 7 _aUNH
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072 7 _aUND
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072 7 _aCOM030000
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082 0 4 _a025.04
_223
100 1 _aFang, Yixiang.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aCohesive Subgraph Search Over Large Heterogeneous Information Networks
_h[electronic resource] /
_cby Yixiang Fang, Kai Wang, Xuemin Lin, Wenjie Zhang.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aXIX, 74 p. 20 illus., 5 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aInformation storage and retrieval systems.
650 0 _aComputer science
_xMathematics.
650 0 _aDiscrete mathematics.
650 0 _aGraph theory.
650 1 4 _aInformation Storage and Retrieval.
650 2 4 _aDiscrete Mathematics in Computer Science.
650 2 4 _aGraph Theory.
700 1 _aWang, Kai.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aLin, Xuemin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aZhang, Wenjie.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030975678
776 0 8 _iPrinted edition:
_z9783030975692
830 0 _aSpringerBriefs in Computer Science,
_x2191-5776
856 4 0 _uhttps://doi.org/10.1007/978-3-030-97568-5
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c185711
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