Fundamentals of Big Data Network Analysis for Research and Industry
(Sprache: Englisch)
Fundamentals of Big Data Network Analysis for Research and IndustryHyunjoung Lee, Institute of Green Technology, Yonsei University, Republic of KoreaIl Sohn, Material Science and Engineering, Yonsei University, Republic of KoreaPresents the methodology of...
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Fundamentals of Big Data Network Analysis for Research and IndustryHyunjoung Lee, Institute of Green Technology, Yonsei University, Republic of KoreaIl Sohn, Material Science and Engineering, Yonsei University, Republic of KoreaPresents the methodology of big data analysis using examples from research and industryThere 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 dataFundamentals 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
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and identify potential relationships among data sets.
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Inhaltsverzeichnis zu „Fundamentals of Big Data Network Analysis for Research and Industry “
1. Why Big Data? 141.1 Big Data 141.2 What creates Big Data? 181.3 How do we use Big Data? 201.4 Essential issues related to Big Data 232. Basic Programs for Analyzing Networks 252.1 UCINET 252.2 NetMiner 302.3 R 352.4 Gephi 372.5 NodeXL 403 Understanding Network Analysis 423.1 Defining social network analysis 423.2 Basic SNA concepts 443.2.1 Basic terminology 443.2.2 Representation of a network 453.3 Social network data 473.3.1 1-mode and 2-mode networks 483.3.2 Attributes and weights 493.3.3 Network data form 504 Research Methods using SNA 524.1 SNA research procedures 524.2 Identifying the research problem and developing hypotheses 534.2.1 Identifying the research problem 534.2.2 Developing hypotheses 544.3 Research design 554.3.1 Defining the network model 554.3.2 Establishing network boundaries 574.3.3 Measurement Evaluation 584.4 Acquisition of network data 604.4.1 Survey 604.4.2 Interview, observation, and experiment 624.4.3 Existing data 624.5 Data cleansing 654.5.1 Extraction of the node and link 664.5.2 Merging and separation of data 674.5.3 Directional transformation in the link 694.5.4 Transformation of the weights in links 704.5.5 Transformation of the 2-mode network to a 1-mode network 725 Position and Structure 755.1 Position 755.1.1 Degree centrality 775.1.2 Closeness centrality 845.1.3 Betweenness centrality 875.1.4 Prestige centrality 895.1.4 Broker 925.2 Cohesive subgroup 935.2.1 Component 945.2.2 Community 955.2.3 Clique 975.2.4 k-core 986 Connectivity and Role 1006.1 Connectivity 1006.1.1 Connectivity 1006.1.2 Reciprocity 1046.1.3 Transitivity 1056.1.4 Assortativity 1076.1.5 Network Properties 1086.2 Role 1086.2.1 Structural equivalence 1096.2.2 Automorphic equivalence 1126.2.3 Role equivalence 1136.2.4 Regular equivalence 1146.2.5 Block modeling 1177 Data Pre-processing using NetMiner 1207.1 Sample Data 1207.1.1. Org_Net_Tiny1 1207.1.2. 02.Org_Net_Tiny2 1217.1.3. 03.Org_Net_Tiny3 1227.2 Main concept 1237.2.1. Data structure 1237.2.2. Creating
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data 1257.2.3. Inserting data 1277.2.4. Importing data 1307.3 Data Preprocessing 1327.3.1. Change of link 1327.3.2. Data Merge and Split 1408 Network Analysis using NetMiner 1448.1 Centrality and Cohesive subgroup 1448.1.1. Centrality 1448.1.2. Cohesive subgroup 1528.2 Connectivity and Equivalence 1578.2.1. Connectivity 1578.2.2. Equivalence 1618.3 Visualization and Exploratory analysis 1678.3.1. Visualization 1678.3.2. Transformation of the 2-mode network to a 1-mode network 174Appendix A. Visualization 1761. Spring Algorithm 1762. Multidimensional Scaling Algorithm 1783. Cluster Algorithm 1784. Layered Algorithm 1795. Circular Algorithm 1806. Simple Algorithm 181Appendix B. Case Study Knowledge Structure of Steel Research 182Subject Index 196Reference 200
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Bibliographische Angaben
- Autoren: Lee Hyun-Jung , Il Sohn
- 216 Seiten, Maße: 21,6 x 22,9 cm, Gebunden, Englisch
- Verlag: John Wiley & Sons, Inc.
- ISBN-10: 1119015588
- ISBN-13: 9781119015581
- Erscheinungsdatum: 27.11.2015
Sprache:
Englisch
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