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＜電子ブック＞
Cohesive Subgraph Computation over Large Sparse Graphs : Algorithms, Data Structures, and Programming Techniques / by Lijun Chang, Lu Qin
(Springer Series in the Data Sciences. ISSN:23655682)

版	1st ed. 2018.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2018
大きさ	XII, 107 p. 21 illus., 1 illus. in color : online resource
著者標目	*Chang, Lijun author Qin, Lu author SpringerLink (Online service)
件　名	LCSH:Algorithms LCSH:Artificial intelligence—Data processing FREE:Algorithms FREE:Data Science
一般注記	Introduction -- Linear Heap Data Structures -- Minimum Degree-based Core Decomposition -- Average Degree-based Densest Subgraph Computation -- Higher-order Structure-based Graph Decomposition -- Edge Connectivity-based Graph Decomposition This book is considered the first extended survey on algorithms and techniques for efficient cohesive subgraph computation. With rapid development of information technology, huge volumes of graph data are accumulated. An availability of rich graph data not only brings great opportunities for realizing big values of data to serve key applications, but also brings great challenges in computation. Using a consistent terminology, the book gives an excellent introduction to the models and algorithms for the problem of cohesive subgraph computation. The materials of this book are well organized from introductory content to more advanced topics while also providing well-designed source codes for most algorithms described in the book. This is a timely book for researchers who are interested in this topic and efficient data structure design for large sparse graph processing. It is also a guideline book for new researchers to get to know the area of cohesive subgraph computation HTTP:URL=https://doi.org/10.1007/978-3-030-03599-0

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