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.
By: Lee, Hyunjoung [author.]
Contributor(s): Sohn, Il [author.]
Language: English Publisher: Chichester, UK : John Wiley & Sons, 2016Edition: First editionDescription: xviii, 196 pages : illustrations ; 24 cmContent type: text Media type: unmediated Carrier type: volumeISBN: 9781119015581 (cloth)Subject(s): Data mining | Big data | Decision making -- Data processingAdditional physical formats: Online version:: Fundamentals of big dataDDC classification: 006.3/12 LOC classification: QA76.9.D343 | L445 2016| Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
BOOK
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COLLEGE LIBRARY | COLLEGE LIBRARY SUBJECT REFERENCE | 006.312 L5101 2016 (Browse shelf) | Available | CITU-CL-47284 |
Includes bibliographical references and index.
Table of Contents
Preface ix
About the Authors xi
List of Figures xiii
List of Tables xvii
1 Why Big Data? 1
1.1 Big Data 1
1.2 What Creates Big Data? 6
1.3 How Do We Use Big Data? 9
1.4 Essential Issues Related to Big Data 13
References 14
2 Basic Programs for Analyzing Networks 15
2.1 UCINET 15
2.2 NetMiner 20
2.3 R 22
2.4 Gephi 28
2.5 NodeXL 31
References 32
3 Understanding Network Analysis 35
3.1 Defining Social Network Analysis 35
3.2 Basic SNA Concepts 37
3.2.1 Basic Terminology 37
3.2.2 Representation of a Network 38
3.3 Social Network Data 40
3.3.1 One ]Mode and Two ]Mode Networks 40
3.3.2 Attributes and Weights 42
3.3.3 Network Data Form 42
References 44
4 Research Methods Using SNA 45
4.1 SNA Research Procedures 46
4.2 Identifying the Research Problem and Developing Hypotheses 47
4.2.1 Identifying the Research Problem 47
4.2.2 Developing Hypotheses 47
4.3 Research Design 49
4.3.1 Defining the Network Model 49
4.3.2 Establishing Network Boundaries 51
4.3.3 Measurement Evaluation 52
4.4 Acquisition of Network Data 54
4.4.1 Survey 54
4.4.2 Interview, Observation, and Experiment 55
4.4.3 Existing Data 56
4.5 Data Cleansing 58
4.5.1 Extraction of the Node and Link 59
4.5.2 Merging and Separation of Data 59
4.5.3 Directional Transformation in the Link 61
4.5.4 Transformation of the Weights in Links 64
4.5.5 Transformation of the Two ]Mode Network to a One ]Mode Network 66
References 69
5 Position and Structure 71
5.1 Position 71
5.1.1 Degree Centrality 72
5.1.2 Closeness Centrality 82
5.1.3 Betweenness Centrality 84
5.1.4 Prestige Centrality 85
5.1.5 Broker 88
5.2 Cohesive Subgroup 91
5.2.1 Component 91
5.2.2 Community 92
5.2.3 Clique 93
5.2.4 k ]Core 95
References 96
6 Connectivity and Role 97
6.1 Connection Analysis 98
6.1.1 Connectivity 98
6.1.2 Reciprocity 99
6.1.3 Transitivity 102
6.1.4 Assortativity 104
6.1.5 Network Properties 104
6.2 Role 104
6.2.1 Structural Equivalence 105
6.2.2 Automorphic Equivalence 107
6.2.3 Role Equivalence 109
6.2.4 Regular Equivalence 111
6.2.5 Block Modeling 115
References 117
7 Data Structure in NetMiner 119
7.1 Sample Data 119
7.1.1 01.Org_Net_Tiny1 120
7.1.2 02.Org_Net_Tiny2 120
7.1.3 03.Org_Net_Tiny3 121
7.2 Main Concept 122
7.2.1 Data Structure 122
7.2.2 Creating Data 124
7.2.3 Inserting Data 125
7.2.4 Importing Data 129
7.3 Data Preprocessing 130
7.3.1 Change of Link 130
7.3.2 Extraction and Reordering of the Node and Link 133
7.3.3 Data Merge and Split 136
Reference 140
8 Network Analysis Using NetMiner 141
8.1 Centrality and Cohesive Subgroup 141
8.1.1 Centrality 141
8.1.2 Cohesive Subgroup 147
8.2 Connectivity and Equivalence 153
8.2.1 Connectivity 153
8.2.2 Equivalence 156
8.3 Visualization and Exploratory Analysis 161
8.3.1 Visualization 161
8.3.2 Transformation of the Two ]Mode Network to a One ]Mode Network 168
Appendix A Visualization 171
A.1 Spring Algorithm 171
A.2 Multidimensional Scaling Algorithm 173
A.3 Cluster Algorithm 173
A.4 Layered Algorithm 174
A.5 Circular Algorithm 174
A.6 Simple Algorithm 175
References 176
Appendix B Case Study: Knowledge Structure of Steel Research 179
Index 193
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.
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