Mohammad Gouse Galety, PhD, is an assistant professor in the Information Technology Department, Catholic University in Erbil, Erbil, Iraq.

Chiai Al-Atroshi is a lecturer in the Educational Counseling and Psychology Department, University of Duhok, Duhok, Iraq.

Bunil Kumar Balabantaray, PhD, is an assistant professor in the Department of Computer Science and Engineering, National Institute of Technology Meghalaya, India.

Sachi Nandan Mohanty, PhD, is an associate professor in the Department of Computer Science & Engineering at Vardhaman College of Engineering (Autonomous), Hyderabad, India.

Preface xi 1 Overview of Social Network Analysis and Different Graph File Formats 1Abhishek 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 19Agathiya 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 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 37Arman 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 51Vasanthakumar 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 63Sailee Bhambere 5.1 Introduction 64 5.2 Prognostic Analytics-Healthcare 64 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 83Sasmita 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 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 109Gouse 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 ZI 125 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 133R. 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 9 Sentiment Analysis-Based Extraction of Real-Time Social Media Information From Twitter Using Natural Language Processing 149Madhuri Thimmapuram, Devasish Pal and Gouse Baig Mohammad 9.1 Introduction 150 9.1.1 Applications for Social Media 153 9.1.2 Social Media Data Challenges 154 9.2 Literature Survey 157 9.2.1 Techniques in Sentiment Analysis 164 9.3 Implementation and Results 166 9.3.1 Online Commerce 166 9.3.2 Feature Extraction 167 9.3.3 Hashtags 167 9.3.4 Punctuations 167 9.4 Conclusion 168 9.5 Future Scope 171 References 171 10 Cascading Behavior: Concept and Models 175Bithika Bishesh 10.1 Introduction 175 10.2 Cascade Networks 177 10.3 Importance of Cascades 178 10.4 Purposes for Studying Cascades 179 10.5 Collective Action 179 10.6 Cascade Capacity 180 10.7 Models of Network Cascades 180 10.7.1 Decision-Based Diffusion Models 181 10.7.2 Probabilistic Model of Cascade 181 10.7.3 Linear Threshold Model 183 10.7.4 Independent Cascade Model 183 10.7.5 SIR Epidemic Model 184 10.8 Centrality 186 10.9 Cascading Failures 189 10.10 Cascading Behavior Example Using Python 189 10.11 Conclusion 192 References 202 11 Exploring Social Networking Data Sets 205Arulkumar N., Joy Paulose, Mohammad Gouse Galety, Manimaran A., S. Saravanan and Saleem Raja A. 11.1 Introduction 206 11.1.1 Network Theory 206 11.1.2 Social Network Analysis 207 11.2 Establishing a Social Network 208 11.2.1 Designing the Symmetric Social Network 208 11.2.2 Creating an Asymmetric Social Network 210 11.2.3 Implementing and Visualizing Weighted Social Networks 212 11.2.4 Developing the Multigraph for Social Networks 213 11.3 Connectivity of Users in Social Networks 214 11.3.1 The Degree to which a Network Exists 214 11.3.2 Coefficient of Clustering 215 11.3.3 The Shortest Routes and Length Between Two Nodes 215 11.3.4 Eccentricity Distribution of a Node in a Social Network 217 11.3.5 Scale-Independent Social Networks 218 11.3.6 Transitivity 218 11.4 Centrality Measures in Social Networks 218 11.4.1 Centrality by Degree 219 11.4.2 Centrality by Eigenvectors 219 11.4.3 Centrality by Betweenness 220 11.4.4 Closeness to All Other Nodes 220 11.5 Case Study of Facebook 221 11.6 Conclusion 226 References 227 Index 229