Social Network Analysis

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 xi1 Overview of Social Network Analysis and Different Graph File Formats 1Abhishek B. and Sumit Hirve1.1 Introduction—Social Network Analysis 21.2 Important Tools for the Collection and Analysis of Online Network Data 31.3 More on the Python Libraries and Associated Packages 91.4 Execution of SNA in Terms of Real-Time Application: Implementation in Python 131.5 Clarity Toward the Indices Employed in the Social Network Analysis 141.5.1 Centrality 141.5.2 Transitivity and Reciprocity 151.5.3 Balance and Status 151.6 Conclusion 15References 152 Introduction To Python for Social Network Analysis 19Agathiya Raja, Gavaskar Kanagaraj and Mohammad Gouse Galety2.1 Introduction 202.2 SNA and Graph Representation 212.2.1 The Common Representation of Graphs 212.2.2 Important Terms to Remember in Graph Representation 232.3 Tools To Analyze Network 242.3.1 MS Excel 242.3.2 Ucinet 262.4 Importance of Analysis 262.5 Scope of Python in SNA 262.5.1 Comparison of Python With Traditional Tools 272.6 Installation 272.6.1 Good Practices 282.7 Use Case 292.7.1 Facebook Case Study 302.8 Real-Time Product From SNA 322.8.1 Nevaal Maps 33References 343 Handling Real-World Network Data Sets 37Arman Abouali Galehdari, Behnaz Moradi and Mohammad Gouse Galety3.1 Introduction 373.2 Aspects of the Network 383.3 Graph 413.3.1 Node, Edges, and Neighbors 413.3.2 Small-World Phenomenon 423.4 Scale-Free Network 433.5 Network Data Sets 463.6 Conclusion 49References 494 Cascading Behavior in Networks 51Vasanthakumar G. U.4.1 Introduction 514.1.1 Types of Data Generated in OSNs 524.1.2 Unstructured Data 524.1.3 Tools for Structuring the Data 534.2 User Behavior 534.2.1 Profiling 544.2.2 Pattern of User Behavior 544.2.3 Geo-Tagging 554.3 Cascaded Behavior 564.3.1 Cross Network Behavior 564.3.2 Pattern Analysis 584.3.3 Models for Cascading Pattern 59References 605 Social Network Structure and Data Analysis in Healthcare 63Sailee Bhambere5.1 Introduction 645.2 Prognostic Analytics—Healthcare 645.3 Role of Social Media for Healthcare Applications 655.4 Social Media in Advanced Healthcare Support 675.5 Social Media Analytics 675.5.1 Phases Involved in Social Media Analytics 685.5.2 Metrics of Social Media Analytics 695.5.3 Evolution of NIHR 705.6 Conventional Strategies in Data Mining Techniques 715.6.1 Graph Theoretic 725.6.2 Opinion Evaluation in Social Network 745.6.3 Sentimental Analysis 755.7 Research Gaps in the Current Scenario 755.8 Conclusion and Challenges 77References 786 Pragmatic Analysis of Social Web Components on Semantic Web Mining 83Sasmita Pani, Bibhuprasad Sahu, Jibitesh Mishra, Sachi Nandan Mohanty and Amrutanshu Panigrahi6.1 Introduction 846.2 Background 876.2.1 Web 876.2.2 Agriculture Information Systems 886.2.3 Ontology in Web or Mobile Web 906.3 Proposed Model 906.3.1 Developing Domain Ontology 916.3.2 Building the Agriculture Ontology with OWL-DL 946.3.2.1 Building Class Axioms 946.3.3 Building Object Property Between the Classes in OWL-DL 956.3.3.1 Building Object Property Restriction in OWL-DL 966.3.4 Developing Social Ontology 976.3.4.1 Building Class Axioms 996.3.4.2 Analysis of Social Web Components on Domain Ontology Under Agriculture System 1006.4 Building Social Ontology Under the Agriculture Domain 1006.4.1 Building Disjoint Class 1006.4.2 Building Object Property 1036.5 Validation 1046.6 Discussion 1046.7 Conclusion and Future Work 105References 1067 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. Dileep7.1 Introduction 1107.1.1 Cascade Blogosphere Information 1117.1.2 Viral Marketing Cascades 1127.1.3 Cascade Network Building 1137.1.4 Cascading Behavior Empirical Research 1137.1.5 Cascades and Impact Nodes Detection 1147.1.6 Topologies of Cascade Networks 1147.1.7 Proposed Scheme Contributions 1177.2 Literature Survey 1187.2.1 Network Failures 1227.3 Methodology 1237.3.1 K-Means Clustering for Anomaly Detection 1237.3.2 C4.5 Decision Trees Anomaly Detection 1247.4 Implementation 1257.4.1 Training Phase ZI 1257.4.2 Testing Phase 1267.5 Results and Discussion 1277.5.1 Data Sets 1277.5.2 Experiment Evaluation 1277.6 Conclusion 127References 1288 Machine Learning Approach To Forecast the Word in Social Media 133R. Vijaya Prakash8.1 Introduction 1338.2 Related Works 1358.3 Methodology 1358.3.1 TF-IDF Technique 1368.3.2 Times Series 1378.4 Results and Discussion 1388.5 Conclusion 141References 1459 Sentiment Analysis-Based Extraction of Real-Time Social Media Information From Twitter Using Natural Language Processing 149Madhuri Thimmapuram, Devasish Pal and Gouse Baig Mohammad9.1 Introduction 1509.1.1 Applications for Social Media 1539.1.2 Social Media Data Challenges 1549.2 Literature Survey 1579.2.1 Techniques in Sentiment Analysis 1649.3 Implementation and Results 1669.3.1 Online Commerce 1669.3.2 Feature Extraction 1679.3.3 Hashtags 1679.3.4 Punctuations 1679.4 Conclusion 1689.5 Future Scope 171References 17110 Cascading Behavior: Concept and Models 175Bithika Bishesh10.1 Introduction 17510.2 Cascade Networks 17710.3 Importance of Cascades 17810.4 Purposes for Studying Cascades 17910.5 Collective Action 17910.6 Cascade Capacity 18010.7 Models of Network Cascades 18010.7.1 Decision-Based Diffusion Models 18110.7.2 Probabilistic Model of Cascade 18110.7.3 Linear Threshold Model 18310.7.4 Independent Cascade Model 18310.7.5 SIR Epidemic Model 18410.8 Centrality 18610.9 Cascading Failures 18910.10 Cascading Behavior Example Using Python 18910.11 Conclusion 192References 20211 Exploring Social Networking Data Sets 205Arulkumar N., Joy Paulose, Mohammad Gouse Galety, Manimaran A., S. Saravanan and Saleem Raja A.11.1 Introduction 20611.1.1 Network Theory 20611.1.2 Social Network Analysis 20711.2 Establishing a Social Network 20811.2.1 Designing the Symmetric Social Network 20811.2.2 Creating an Asymmetric Social Network 21011.2.3 Implementing and Visualizing Weighted Social Networks 21211.2.4 Developing the Multigraph for Social Networks 21311.3 Connectivity of Users in Social Networks 21411.3.1 The Degree to which a Network Exists 21411.3.2 Coefficient of Clustering 21511.3.3 The Shortest Routes and Length Between Two Nodes 21511.3.4 Eccentricity Distribution of a Node in a Social Network 21711.3.5 Scale-Independent Social Networks 21811.3.6 Transitivity 21811.4 Centrality Measures in Social Networks 21811.4.1 Centrality by Degree 21911.4.2 Centrality by Eigenvectors 21911.4.3 Centrality by Betweenness 22011.4.4 Closeness to All Other Nodes 22011.5 Case Study of Facebook 22111.6 Conclusion 226References 227Index 229