EAN: 9783030975678

SPRINGERBRIEFS IN COMPUTER SCIENCE COHESIVE S...

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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