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Books > Computing & IT > Applications of computing > Databases
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Cohesive Subgraph Search Over Large Heterogeneous Information Networks (Paperback, 1st ed. 2022)
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Cohesive Subgraph Search Over Large Heterogeneous Information Networks (Paperback, 1st ed. 2022)
Series: SpringerBriefs in Computer Science
Expected to ship within 12 - 19 working days
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This 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.
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