Note: Intro -- Preface -- Acknowledgements -- Contents -- Diffusion of Information in Social Networks -- 1 Introduction -- 2 Models of Information Diffusion in Social Networks -- 2.1 Mathematical Modeling of Information Diffusion -- 2.2 Influencers in Social Networks -- 2.3 Influencers and Controlling Mis-information Spread in Social Networks -- 2.4 Multiple Sources of Diffusion and the Problem of Information Confusion -- 3 Locating the Source of Diffusion -- 4 Summary -- References -- Structure and Evolution of Online Social Networks -- 1 Introduction -- 2 Topological Properties of Social Networks -- 2.1 Topological Properties Used to Characterize Large Networks -- 2.2 Topological Properties of OSNs -- 3 Evolution of Social Networks -- 3.1 Evolution Models for Complex Networks -- 3.2 Evolution Models for OSNs -- 4 Evolution of OSNs in Presence of Restrictions on Node-degree -- 4.1 The Restriction in Twitter and Its Effects -- 4.2 Evolution Model in Presence of Restrictions on Node-degree -- 5 Summary -- References -- Machine Learning for Auspicious Social Network Mining -- 1 Introduction -- 2 Representation of Network Data -- 3 Different Aspects of Social Network Analysis -- 3.1 Different Measures of Social Network -- 3.2 Essential Problems and Algorithms -- 4 Statistical Models for Social Network Analysis: Probability and Random Walks -- 5 Groups and Substructures in Social Networks -- 5.1 Bipartite Cores - Identifying Communities -- 6 Machine Learning Based Approaches for Networked Data Mining -- 6.1 Social Analytical Data Preparation with Machine Learning -- 6.2 Machine Learning for Networked Data Mining -- 6.3 Some Highlited Research and Findings Using Machine Learning -- 7 Conclusion -- References -- Testing Community Detection Algorithms: A Closer Look at Datasets -- 1 Introduction -- 2 Evaluating Community Detection Methods. , 2.1 Quality Measures -- 2.2 Similarity Measure -- 3 Social Network Datasets -- 3.1 The Zachary Karate Club -- 3.2 The Bottlenose Dolphin Network -- 3.3 American College Football Network -- 3.4 Online Social Networks -- 4 Conclusion -- References -- Societal Networks: The Networks of Dynamics of Interpersonal Associations -- 1 Introduction -- 2 Background -- 3 Graphs and Digraphs -- 3.1 Representations of Sigraphs and Sidigraphs -- 3.2 Dynamics of a Social System -- 3.3 Cognitive Balance and Sidigraphs -- 3.4 The Phenomenon of Clustering -- 4 Social Networks -- 5 Societal Networks -- 5.1 The Key Principle -- 5.2 The Origin -- 5.3 Extension to a Formal Model -- 5.4 Structural Equivalence -- 5.5 Reduced Networks -- 5.6 Class Structures -- 5.7 Self-balancing Networks -- 5.8 Long-Term Evaluation -- 6 Some Recent Developments in Dynamic Social Networks -- 6.1 Mining Periodic Behaviour in Dynamic Social Networks -- 6.2 Periodic Subgraph Mining Problem -- 6.3 Framework for Analysis of Dynamic Social Networks -- 7 Problems for Further Study -- 8 Conclusion -- References -- Methods of Tracking Online Community in Social Network -- 1 Introduction -- 2 Related Work -- 2.1 Subgroup Identification -- 2.2 Clustering and Partitioning of Subgroups -- 2.3 Similarity -- 2.4 Behavioural Measures of Community -- 3 Existing Methods and Framework for Tracking Communities -- 3.1 Social Cohesion Analysis of Networks (SCAN) -- 3.2 DISSECT (Data-Intensive Socially Similar Evolving Community Tracker) -- 3.3 Framework for DISSECT Method -- 4 Conclusion -- References -- Social Network Analysis Approach for Studying Caste, Class and Social Support in Rural Jharkhand andWest Bengal: An Empirical Attempt -- 1 Introduction -- 2 Methodology -- 2.1 Village Selection for the Study -- 2.2 Data Collection and Level of Analysis. , 2.3 Limitations of Analysis by Exact Caste Community (CC) and Source of Livelihood (SL) -- 2.4 Formation of Categories of CC and SL -- 3 Brief Historical Background of the Villages -- 3.1 Location and other Facilities -- 3.2 Settlement Patterns of the Villages -- 4 Social Stratification of the Villages -- 4.1 By Caste Community -- 4.2 Stratification by Source of Livelihood (SL) Categories and Ownership of Land -- 4.3 What Patterns Observed -- 4.4 Inter-relationship of CC and SL Categories in Villages -- 5 Parameters of Life and Living of the Villagers Required for Social Network -- 5.1 Type and Timing of Needs and Urgencies -- 5.2 Availability of Help from Formal Institutions to Satisfy Urgent Needs -- 5.3 How One Decides to Approach Whom for Help or Support -- 5.4 Why Help Is Provided -- 5.5 Distance of Spread of Sources of Helps and Assistance -- 6 Findings from Social Network Analysis -- 6.1 From Whom Help Was Sought -- 6.2 Amount/Quantity and Type of Help Sought -- 6.3 Purpose of Help -- 7 Conclusions -- References -- Evaluating the Propagation Strength of Malicious Metaphor in Social Network: Flow Through Inspiring Influence of Members -- 1 Introduction -- 2 Fundamental of Social Network: Graph Theory Perspective -- 2.1 Co-Evolution of Social and Affiliation Network -- 3 Exploring Mathematical Treatments -- 3.1 Proposed Algorithm -- 3.2 Experimental Results and Discussion -- 3.3 Comparison with Statistical Model -- 4 Graphical Representation -- 5 Conclusion and Further Scope of Research -- References -- Social Network Analysis: A Methodology for Studying Terrorism -- 1 Introduction -- 2 Two Phases in the Development of Social Network Analysis -- 3 Social Network Analysis and Terrorism -- 4 Data Collection and Data Sources -- 4.1 Structured Data Sources -- 4.2 Unstructured Relational Data Sources. , 5 Simulation, Modelling and Computational Terrorism -- 6 Essentials of Social Networks and Centrality Measures -- 7 Case Studies -- 7.1 Case I: The 9/11 Attack on the Trade Towers in New York -- 7.2 Case II: Mapping a Network of Terrorist Organizations in India -- 8 Conclusions -- References -- Privacy and Anonymization in Social Networks -- 1 Introduction -- 1.1 Chapter Structure -- 2 Social Networks -- 3 Privacy in Social Networks -- 3.1 How Users Lose Control of Their Privacy -- 4 Privacy in Published Social Network Data -- 4.1 Social Network Representation -- 4.2 Social Network Analysis -- 4.3 Anonymization -- 4.4 Challenges in Anonymizing Social Networks Data -- 5 Private Information in Published Social Network Data -- 6 Background-Knowledge Attacks -- 6.1 Background Knowledge of Adversaries -- 7 Related Work -- 8 Problem Formulation -- 9 GASNA: Greedy Algorithm for Social Network Anonymization [24,27] -- 9.1 Clustering Phase -- 9.2 Adjustment Phase -- 9.3 Anonymization Phase -- 10 Empirical Evaluation of GASNA -- 11 Comparison with the Literature -- 12 Conclusion and Future Issues -- 12.1 Varying k Value for k-anonymity According to the Degree of the Nodes Being Considered -- 12.2 Anonymity in Modern Social Networks -- 12.3 Partial Anonymity -- References -- On the Use of Brokerage Approach to Discover Influencing Nodes in Terrorist Networks -- 1 Motivation -- 2 Background -- 3 Terrorist Network Mining -- 4 Calculating Centrality Measures -- 4.1 Degree Centrality -- 4.2 Betweenness Centrality -- 4.3 Closeness Centrality -- 4.4 Eigenvector Centrality -- 5 Brokerage -- 6 Roles Detected by Brokerage Approach -- 6.1 Coordinator -- 6.2 Consultant -- 6.3 Gatekeeper -- 6.4 Representative -- 6.5 Liaison -- 7 Experimental Analysis through Brokerage -- 8 Conclusion and Future Work -- References -- Index.