Cohesive Subgraph Computation over Large Sparse Graphs Cohesive Subgraph Computation over Large Sparse Graphs
Springer Series in the Data Sciences

Cohesive Subgraph Computation over Large Sparse Graphs

Algorithms, Data Structures, and Programming Techniques

    • ‏39٫99 US$
    • ‏39٫99 US$

وصف الناشر

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.

النوع
كمبيوتر وإنترنت
تاريخ النشر
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٢٤ ديسمبر
اللغة
EN
الإنجليزية
عدد الصفحات
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الناشر
Springer International Publishing
البائع
Springer Nature B.V.
الحجم
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‫م.ب.‬
Treewidth, Kernels, and Algorithms Treewidth, Kernels, and Algorithms
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Combinatorial Algorithms Combinatorial Algorithms
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Graph-Theoretic Concepts in Computer Science Graph-Theoretic Concepts in Computer Science
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Combinatorial Algorithms Combinatorial Algorithms
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Graph-Theoretic Concepts in Computer Science Graph-Theoretic Concepts in Computer Science
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Combinatorial Optimization and Applications Combinatorial Optimization and Applications
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Databases Theory and Applications Databases Theory and Applications
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Database Systems for Advanced Applications Database Systems for Advanced Applications
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Database Systems for Advanced Applications Database Systems for Advanced Applications
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Database Systems for Advanced Applications Database Systems for Advanced Applications
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Web Technologies and Applications Web Technologies and Applications
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Databases Theory and Applications Databases Theory and Applications
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Statistics with Julia Statistics with Julia
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First-order and Stochastic Optimization Methods for Machine Learning First-order and Stochastic Optimization Methods for Machine Learning
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Data Science for Public Policy Data Science for Public Policy
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Mathematical Foundations for Data Analysis Mathematical Foundations for Data Analysis
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Statistical Inference and Machine Learning for Big Data Statistical Inference and Machine Learning for Big Data
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Statistics in the Public Interest Statistics in the Public Interest
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