Social Network Analysis

249 Pages·2022·15.261 MB·English

by  Galety et al

Social Network Analysis
Scrivener Publishing
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Phillip Carmical ([email protected])
Social Network Analysis
Theory and Applications
Edited by
Mohammad Gouse Galety
Chiai Al Atroshi
Bunil Kumar Balabantaray

and
Sachi Nandan Mohanty
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-83623-0
Cover image: Pixabay.Com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Contents
Preface xi
1 Overview of Social Network Analysis and Different Graph
File Formats 1
Abhishek B. and Sumit Hirve
1.1 Introduction—Social Network Analysis 2
1.2 Important Tools for the Collection and Analysis
of Online Network Data 3
1.3 More on the Python Libraries and Associated Packages 9
1.4 Execution of SNA in Terms of Real-Time Application:
Implementation in Python 13
1.5 Clarity Toward the Indices Employed in the Social
Network Analysis 14
1.5.1 Centrality 14
1.5.2 Transitivity and Reciprocity 15
1.5.3 Balance and Status 15
1.6 Conclusion 15
References 15
2 Introduction To Python for Social Network Analysis 19
Agathiya Raja, Gavaskar Kanagaraj
and Mohammad Gouse Galety
2.1 Introduction 20
2.2 SNA and Graph Representation 21
2.2.1 The Common Representation of Graphs 21
2.2.2 Important Terms to Remember in Graph
Representation 23
2.3 Tools To Analyze Network 24
2.3.1 MS Excel 24
2.3.2 UCINET 26
2.4 Importance of Analysis 26
2.5 Scope of Python in SNA 26
v
vi Contents
2.5.1 Comparison of Python With Traditional Tools 27
2.6 Installation 27
2.6.1 Good Practices 28
2.7 Use Case 29
2.7.1 Facebook Case Study 30
2.8 Real-Time Product From SNA 32
2.8.1 Nevaal Maps 33
References 34
3 Handling Real-World Network Data Sets 37
Arman Abouali Galehdari, Behnaz Moradi
and Mohammad Gouse Galety
3.1 Introduction 37
3.2 Aspects of the Network 38
3.3 Graph 41
3.3.1 Node, Edges, and Neighbors 41
3.3.2 Small-World Phenomenon 42
3.4 Scale-Free Network 43
3.5 Network Data Sets 46
3.6 Conclusion 49
References 49
4 Cascading Behavior in Networks 51
Vasanthakumar G. U.
4.1 Introduction 51
4.1.1 Types of Data Generated in OSNs 52
4.1.2 Unstructured Data 52
4.1.3 Tools for Structuring the Data 53
4.2 User Behavior 53
4.2.1 Profiling 54
4.2.2 Pattern of User Behavior 54
4.2.3 Geo-Tagging 55
4.3 Cascaded Behavior 56
4.3.1 Cross Network Behavior 56
4.3.2 Pattern Analysis 58
4.3.3 Models for Cascading Pattern 59
References 60
5 Social Network Structure and Data Analysis in Healthcare 63
Sailee Bhambere
5.1 Introduction 64
5.2 Prognostic Analytics—Healthcare 64
Contents vii
5.3 Role of Social Media for Healthcare Applications 65
5.4 Social Media in Advanced Healthcare Support 67
5.5 Social Media Analytics 67
5.5.1 Phases Involved in Social Media Analytics 68
5.5.2 Metrics of Social Media Analytics 69
5.5.3 Evolution of NIHR 70
5.6 Conventional Strategies in Data Mining Techniques 71
5.6.1 Graph Theoretic 72
5.6.2 Opinion Evaluation in Social Network 74
5.6.3 Sentimental Analysis 75
5.7 Research Gaps in the Current Scenario 75
5.8 Conclusion and Challenges 77
References 78
6 Pragmatic Analysis of Social Web Components
on Semantic Web Mining 83
Sasmita Pani, Bibhuprasad Sahu, Jibitesh Mishra,
Sachi Nandan Mohanty and Amrutanshu Panigrahi
6.1 Introduction 84
6.2 Background 87
6.2.1 Web 87
6.2.2 Agriculture Information Systems 88
6.2.3 Ontology in Web or Mobile Web 90
6.3 Proposed Model 90
6.3.1 Developing Domain Ontology 91
6.3.2 Building the Agriculture Ontology with OWL-DL 94
6.3.2.1 Building Class Axioms 94
6.3.3 Building Object Property Between the Classes
in OWL-DL 95
6.3.3.1 Building Object Property Restriction
in OWL-DL 96
6.3.4 Developing Social Ontology 97
6.3.4.1 Building Class Axioms 99
6.3.4.2 Analysis of Social Web Components
on Domain Ontology Under
Agriculture System 100
6.4 Building Social Ontology Under the Agriculture Domain 100
6.4.1 Building Disjoint Class 100
6.4.2 Building Object Property 103
6.5 Validation 104
6.6 Discussion 104
viii Contents
6.7 Conclusion and Future Work 105
References 106
7 Classification of Normal and Anomalous Activities
in a Network by Cascading C4.5 Decision Tree
and K-Means Clustering Algorithms 109
Gouse Baig Mohammad, S. Shitharth and P. Dileep
7.1 Introduction 110
7.1.1 Cascade Blogosphere Information 111
7.1.2 Viral Marketing Cascades 112
7.1.3 Cascade Network Building 113
7.1.4 Cascading Behavior Empirical Research 113
7.1.5 Cascades and Impact Nodes Detection 114
7.1.6 Topologies of Cascade Networks 114
7.1.7 Proposed Scheme Contributions 117
7.2 Literature Survey 118
7.2.1 Network Failures 122
7.3 Methodology 123
7.3.1 K-Means Clustering for Anomaly Detection 123
7.3.2 C4.5 Decision Trees Anomaly Detection 124
7.4 Implementation 125
7.4.1 Training Phase Z 125
i
7.4.2 Testing Phase 126
7.5 Results and Discussion 127
7.5.1 Data Sets 127
7.5.2 Experiment Evaluation 127
7.6 Conclusion 127
References 128
8 Machine Learning Approach To Forecast the Word
in Social Media 133
R. Vijaya Prakash
8.1 Introduction 133
8.2 Related Works 135
8.3 Methodology 135
8.3.1 TF-IDF Technique 136
8.3.2 Times Series 137
8.4 Results and Discussion 138
8.5 Conclusion 141
References 145