Fundamentals of big data : network analysis for research and industry / Hyunjoung Lee, Institute of Green Technology, Yonsei University, Republic of Korea, Il Sohn, Material Science and Engineering, Yonsei University, Republic of Korea.

Format:

Book

Author/Creator:

Lee, Hyunjoung, author.

Sohn, Il, author.

Series:

THEi Wiley ebooks.

Language:

English

Subjects (All):

Data mining.

Big data.

Decision making--Data processing.

Decision making.

Physical Description:

1 online resource (217 pages) : illustrations

Edition:

First edition.

Place of Publication:

Chichester, UK : John Wiley & Sons, 2016.

Language Note:

English

System Details:

Access using campus network via VPN at home (THEi Users Only).

Summary:

Presents the methodology of big data analysis using examples from research and industry There are large amounts of data everywhere, and the ability to pick out crucial information is increasingly important. Contrary to popular belief, not all information is useful; big data network analysis assumes that data is not only large, but also meaningful, and this book focuses on the fundamental techniques required to extract essential information from vast datasets. Featuring case studies drawn largely from the iron and steel industries, this book offers practical guidance which will enable readers to easily understand big data network analysis. Particular attention is paid to the methodology of network analysis, offering information on the method of data collection, on research design and analysis, and on the interpretation of results. A variety of programs including UCINET, NetMiner, R, NodeXL, and Gephi for network analysis are covered in detail. Fundamentals of Big Data Network Analysis for Research and Industry looks at big data from a fresh perspective, and provides a new approach to data analysis. This book: * Explains the basic concepts in understanding big data and filtering meaningful data * Presents big data analysis within the networking perspective * Features methodology applicable to research and industry * Describes in detail the social relationship between big data and its implications * Provides insight into identifying patterns and relationships between seemingly unrelated big data Fundamentals of Big Data Network Analysis for Research and Industry will prove a valuable resource for analysts, research engineers, industrial engineers, marketing professionals, and any individuals dealing with accumulated large data whose interest is to analyze and identify potential relationships among data sets.

Contents:

Intro

Title Page

Copyright Page

Contents

Preface

About the Authors

List of Figures

List of Tables

Chapter 1 Why Big Data?

1.1 Big Data

1.2 What Creates Big Data?

1.3 How Do We Use Big Data?

1.4 Essential Issues Related to Big Data

References

Chapter 2 Basic Programs for Analyzing Networks

2.1 UCINET

2.2 NetMiner

2.3 R

2.4 Gephi

2.5 NodeXL

Chapter 3 Understanding Network Analysis

3.1 Defining Social Network Analysis

3.2 Basic SNA Concepts

3.2.1 Basic Terminology

3.2.2 Representation of a Network

3.3 Social Network Data

3.3.1 One-Mode and Two-Mode Networks

3.3.2 Attributes and Weights

3.3.3 Network Data Form

Chapter 4 Research Methods Using SNA

4.1 SNA Research Procedures

4.2 Identifying the Research Problem and Developing Hypotheses

4.2.1 Identifying the Research Problem

4.2.2 Developing Hypotheses

4.3 Research Design

4.3.1 Defining the Network Model

4.3.2 Establishing Network Boundaries

4.3.3 Measurement Evaluation

4.4 Acquisition of Network Data

4.4.1 Survey

4.4.2 Interview, Observation, and Experiment

4.4.3 Existing Data

4.5 Data Cleansing

4.5.1 Extraction of the Node and Link

4.5.2 Merging and Separation of Data

4.5.3 Directional Transformation in the Link

4.5.4 Transformation of the Weights in Links

4.5.5 Transformation of the Two-Mode Network to a One-Mode Network

Chapter 5 Position and Structure

5.1 Position

5.1.1 Degree Centrality

5.1.2 Closeness Centrality

5.1.3 Betweenness Centrality

5.1.4 Prestige Centrality

5.1.5 Broker

5.2 Cohesive Subgroup

5.2.1 Component

5.2.2 Community

5.2.3 Clique

5.2.4 k-Core

Chapter 6 Connectivity and Role

6.1 Connection Analysis

6.1.1 Connectivity

6.1.2 Reciprocity.

6.1.3 Transitivity

6.1.4 Assortativity

6.1.5 Network Properties

6.2 Role

6.2.1 Structural Equivalence

6.2.2 Automorphic Equivalence

6.2.3 Role Equivalence

6.2.4 Regular Equivalence

6.2.5 Block Modeling

Chapter 7 Data Structure in NetMiner

7.1 Sample Data

7.1.1 01.Org_Net_Tiny1

7.1.2 02.Org_Net_Tiny2

7.1.3 03.Org_Net_Tiny3

7.2 Main Concept

7.2.1 Data Structure

7.2.2 Creating Data

7.2.3 Inserting Data

7.2.4 Importing Data

7.3 Data Preprocessing

7.3.1 Change of Link

7.3.2 Extraction and Reordering of the Node and Link

7.3.3 Data Merge and Split

Reference

Chapter 8 Network Analysis Using NetMiner

8.1 Centrality and Cohesive Subgroup

8.1.1 Centrality

8.1.2 Cohesive Subgroup

8.2 Connectivity and Equivalence

8.2.1 Connectivity

8.2.2 Equivalence

8.3 Visualization and Exploratory Analysis

8.3.1 Visualization

8.3.2 Transformation of the Two-Mode Network to a One-Mode Network

Appendix A Visualization

A.1 Spring Algorithm

A.2 Multidimensional Scaling Algorithm

A.3 Cluster Algorithm

A.4 Layered Algorithm

A.5 Circular Algorithm

A.6 Simple Algorithm

Appendix B Case Study: Knowledge Structure of Steel Research

Index

EULA.

Notes:

Bibliographic Level Mode of Issuance: Monograph

Includes bibliographical references and index.

Description based on print version record.

ISBN:

9781119015499

1119015499

9781119015451

1119015456

9781119015574

111901557X

OCLC:

946167236