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Cohesive Subgraph Computation over Large Sparse Graphs : Algorithms, Data Structures, and Programming Techniques / / by Lijun Chang, Lu Qin



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Autore: Chang Lijun Visualizza persona
Titolo: Cohesive Subgraph Computation over Large Sparse Graphs : Algorithms, Data Structures, and Programming Techniques / / by Lijun Chang, Lu Qin Visualizza cluster
Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018
Edizione: 1st ed. 2018.
Descrizione fisica: 1 online resource (113 pages)
Disciplina: 511.5
Soggetto topico: Algorithms
Data structures (Computer science)
Data Structures
Persona (resp. second.): QinLu (Computer scientist)
Nota di contenuto: 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.
Sommario/riassunto: 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.
Titolo autorizzato: Cohesive Subgraph Computation over Large Sparse Graphs  Visualizza cluster
ISBN: 3-030-03599-9
Formato: Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione: Inglese
Record Nr.: 9910303449403321
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Serie: Springer Series in the Data Sciences, . 2365-5674