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

    • US$39.99
    • US$39.99

출판사 설명

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.

장르
컴퓨터 및 인터넷
출시일
2018년
12월 24일
언어
EN
영어
길이
119
페이지
출판사
Springer International Publishing
판매자
Springer Nature B.V.
크기
8.1
MB
Databases Theory and Applications Databases Theory and Applications
2019년
Database Systems for Advanced Applications Database Systems for Advanced Applications
2017년
Database Systems for Advanced Applications Database Systems for Advanced Applications
2017년
Database Systems for Advanced Applications Database Systems for Advanced Applications
2017년
Web Technologies and Applications Web Technologies and Applications
2016년
Databases Theory and Applications Databases Theory and Applications
2016년
Statistics with Julia Statistics with Julia
2021년
First-order and Stochastic Optimization Methods for Machine Learning First-order and Stochastic Optimization Methods for Machine Learning
2020년
Data Science for Public Policy Data Science for Public Policy
2021년
Mathematical Foundations for Data Analysis Mathematical Foundations for Data Analysis
2021년
Statistical Inference and Machine Learning for Big Data Statistical Inference and Machine Learning for Big Data
2022년
Statistics in the Public Interest Statistics in the Public Interest
2022년