Principles of Social Networking: The New Horizon and Emerging Challenges
- Length: 454 pages
- Edition: 1
- Language: English
- Publisher: Springer
- Publication Date: 2021-08-19
- ISBN-10: 9811633975
- ISBN-13: 9789811633973
- Sales Rank: #0 (See Top 100 Books)
This book presents new and innovative current discoveries in social networking which contribute enough knowledge to the research community. The book includes chapters presenting research advances in social network analysis and issues emerged with diverse social media data. The book also presents applications of the theoretical algorithms and network models to analyze real-world large-scale social networks and the data emanating from them as well as characterize the topology and behavior of these networks. Furthermore, the book covers extremely debated topics, surveys, future trends, issues, and challenges.
Preface Contents Editors and Contributors 1 Centrality Measures: A Tool to Identify Key Actors in Social Networks 1.1 Introduction 1.2 Centrality Measures 1.2.1 Traditional Centrality Measures 1.2.2 Other Popular Centrality Measures 1.3 Directions of Research 1.3.1 Exact Computation 1.3.2 Estimation 1.3.3 Updating Centrality Scores 1.3.4 Approximation Algorithms for Dynamic Graphs 1.3.5 Parallel and Distributed Computation 1.3.6 Centrality Ordering and Ranking 1.3.7 Weighted Centrality Measures 1.3.8 Group Centrality Measures 1.3.9 Hybrid Centrality Measures 1.3.10 Centrality Improvement and Maximization 1.3.11 Application 1.3.12 Defining New Centrality Measures 1.4 Conclusion References 2 Network Centrality Measures: Role and Importance in Social Networks 2.1 Introduction 2.1.1 What is Social Networking? 2.1.2 Social Networks as Graph 2.1.3 Why Centrality Analysis? 2.2 Network Centrality: Measures and Concepts 2.2.1 Geometric Measures 2.2.2 Spectral Measures 2.2.3 Path-Based Measures 2.3 Experimental Results and Analysis 2.4 Conclusions References 3 Temporal Network Motifs: Structure, Roles, Computational Issues, and Its Applications 3.1 Introduction 3.1.1 Network Motifs 3.1.2 How to Define a Network Motif (Subgraph) 3.1.3 Motifs: Recurrence 3.1.4 Significance of Motif 3.2 Temporal Network 3.2.1 Applications of Temporal Network 3.3 Temporal Motifs 3.3.1 Temporal Motifs of Two and Three Nodes with Three Edges 3.4 Case Study of Pandemic COVID-19 Involving Data Observation and Analysis 3.4.1 Data Analysis 3.5 Conclusion and Future Scope References 4 Link Prediction on Social Networks Based on Centrality Measures 4.1 Introduction 4.2 Preliminaries 4.2.1 Notations 4.2.2 Basic Concepts 4.2.3 Taxonomy of Centrality Measures 4.3 Centrality Measures 4.4 Link Prediction 4.5 Empirical Analysis 4.5.1 Setup Information 4.5.2 Performance Analysis 4.6 Conclusion and Future Directions References 5 Community Detection in Social Networks 5.1 Introduction 5.2 Community Definition 5.3 Community Structure 5.4 Community Detection Methods 5.5 Conclusion References 6 On the Vulnerability of Community Structure in Complex Networks 6.1 Introduction 6.2 Related Work 6.2.1 Community Detection 6.2.2 Community Vulnerability Analysis 6.3 Problem Statement 6.4 Preliminaries 6.5 Proposed Methodology 6.6 Datasets 6.7 Experiments 6.7.1 Modularity 6.7.2 Normalized Mutual Information 6.7.3 Adjusted Rand Index 6.7.4 Task-Based Approach 6.8 Conclusion References 7 Community Detection in Multidimensional and Multilayer Networks 7.1 Introduction 7.2 Representation of Multidimensional Networks and Multilayer Networks 7.3 Important Features to Be Considered 7.4 Community Detection in Heterogeneous Networks 7.4.1 Community Detection in Multilayer Networks 7.4.2 Community Detection in Multidimensional Networks 7.5 Evaluation Strategies 7.5.1 Datasets 7.5.2 Evaluation Metrics 7.6 Conclusion References 8 Viral Marketing: A New Horizon and Emerging Challenges 8.1 Introduction 8.2 Traditional Versus Viral Marketing 8.3 The Viral Marketing Strategy: Word of Mouth 8.4 Characteristics of Viral Marketing Headings 8.5 Benefits and Risk of Viral Marketing 8.6 Planning for Viral Marketing 8.7 Who Is Carrying Out Viral Campaigns for Marketing? 8.8 Implementing Viral Marketing 8.9 Ways to Improve Your Chances of Going Viral 8.10 Examples of Viral Marketing 8.11 Conclusion References 9 Evolving Models for Dynamic Weighted Complex Networks 9.1 Introduction 9.2 Preliminaries 9.2.1 Strength of a Node 9.2.2 Power-Law Distribution 9.2.3 Preferential Attachment Model 9.3 Undirected and Directed Weighted Networks 9.3.1 Power-Law Minimal Model 9.3.2 Fitness Model 9.3.3 Stochastic Model 9.3.4 Incremental Weight-Distribution Model 9.3.5 Node-Deactivation Model 9.3.6 Non-linear Growing Model 9.3.7 Incremental Self-Growing Model 9.3.8 Triad-Formation Model 9.3.9 Traffic Flow-Driven Model 9.3.10 Local World Model 9.3.11 Mutual Attraction Model 9.3.12 Geographical Constraint Model 9.3.13 Age Weighted Model 9.4 Signed Weighted Networks 9.5 Mesoscale Structured Weighted Networks 9.5.1 Community Structured Weighted Networks 9.5.2 Core–Periphery Structured Weighted Networks 9.6 Multilayered Weighted Networks 9.7 Other Miscellaneous Models 9.7.1 Random Weight Assignment Model 9.7.2 Weighted Stochastic Block Model 9.7.3 Edge Preferential Attachment Model 9.7.4 Weighted Fractal Networks 9.7.5 Capacity Constraint Model 9.7.6 Overlapped Clique Evolution Model 9.8 Conclusion References 10 Learning Graph Representations 10.1 Introduction 10.2 Preliminaries 10.3 Convolutional Graph Neural Networks 10.3.1 Spectral Based 10.3.2 Non-Spectral Based 10.4 Graph Autoencoders 10.5 Spatio-Temporal Graph Neural Networks 10.6 Discussion 10.7 Future Directions 10.8 Conclusion References 11 Social Media Data Collection and Quality for Urban Studies 11.1 Introduction 11.1.1 Relevance of Social Networks 11.1.2 Social Networks and Their Data 11.2 Social Networks for Urban Studies 11.2.1 Foursquare 11.2.2 Google Places 11.2.3 Twitter 11.2.4 Airbnb 11.2.5 Instagram 11.3 Data Variables: Diversity and Quality 11.4 Concluding Remarks References 12 Introduction to Sentiment Analysis Covering Basics, Tools, Evaluation Metrics, Challenges, and Applications 12.1 Introduction 12.2 Sentiment Analysis Levels 12.2.1 Word Level 12.2.2 Sentence Level 12.2.3 Document Level 12.2.4 Feature/Aspect Level 12.3 Sentiment Analysis Methodology 12.3.1 Data Collection 12.3.2 Data Pre-processing 12.3.3 Feature Extraction and Selection 12.3.4 Sentiment Analysis Techniques 12.4 Sentiment Analysis Enhancement 12.4.1 Data Pre-processing and Feature Selection 12.4.2 Text Categorization and Summarization 12.4.3 Ontology-Based Approaches 12.4.4 Data Integration 12.4.5 Crowdsourcing 12.4.6 User Characteristics 12.5 Evaluation Metrics 12.6 Sentiment Analysis Tools 12.7 Challenges 12.8 Applications 12.9 Conclusion 12.10 Further Reading References 13 Recent Developments in Sentiment Analysis on Social Networks: Techniques, Datasets, and Open Issues 13.1 Introduction 13.2 Sentiment Analysis Techniques 13.2.1 Machine Learning Approaches 13.2.2 Lexicon-Based Approaches 13.2.3 Hybrid Approaches 13.2.4 Graph-Based Approaches 13.2.5 Other Approaches 13.3 Datasets 13.4 Future Directions 13.5 Conclusion References 14 Misinformation, Fake News and Rumor Detection 14.1 Introduction 14.1.1 Classification of False, Incorrect and Unverified Information 14.1.2 Power of Misinformation, Fake News and Rumors 14.1.3 Role of Social Media Platforms in Dissemination of Misleading Information 14.1.4 Facebook Influenced the U.S. Presidential Elections 14.1.5 Negative Content and Trust 14.2 Literature Review 14.2.1 The Psychology of Misinformation, Fake News and Rumors 14.2.2 Misinformation, Fake News and Rumor Dissemination Process 14.2.3 Identification of Misinformation, Fake News and Rumors 14.2.4 Correction of Misinformation, Fake News and Rumor 14.2.5 Combating Misinformation, Fake News and Rumor 14.3 Discussion 14.3.1 Educating Internet Users 14.3.2 Detection by Platforms and Authorities 14.4 Conclusion References 15 Fake News Detection Techniques for Social Media 15.1 Introduction 15.2 Fake News Detection 15.2.1 Fake News Detection Features 15.2.2 Fake News Detection Categories 15.2.3 Classification Models 15.2.4 Datasets for Fake News Detection 15.3 Conclusion 15.4 Further Reading References 16 Fake News Propagation and Mitigation Techniques: A Survey 16.1 Introduction 16.2 Propagation Models 16.2.1 Independent Cascade Model 16.2.2 Linear Threshold Model 16.2.3 Compartment Spreading Models 16.2.4 Opinion Formation Model 16.3 Fake News Mitigation 16.3.1 Influence Blocking 16.3.2 Truth Campaigning 16.3.3 Mitigation Tools 16.3.4 Social Scientific Studies 16.3.5 Datasets for Mitigation Studies 16.4 Conclusion References 17 Data Privacy and Security in Social Networks 17.1 Introduction 17.2 Privacy 17.2.1 Privacy as a Human Need 17.2.2 Privacy as Human Right 17.3 Data Privacy Laws 17.3.1 Important Definitions Related to Data Privacy 17.4 Data Privacy Versus Data Security 17.4.1 Personal Data Privacy in Data Generation and Data Collection 17.4.2 Personal Data Privacy in Data Processing 17.4.3 Personal Data Privacy in Data Storage 17.4.4 Personal Data Privacy in Transfer of Data 17.5 Data Privacy Threats in Various Fields 17.5.1 Privacy Issues in Health Sector 17.5.2 Privacy Threats and Toys 17.5.3 Privacy Issues in Digital Forensic 17.5.4 Privacy Issues in Image and Video Surveillance 17.6 Data Privacy and Advanced Technologies 17.6.1 Privacy and Internet of Things (IoT) 17.6.2 Privacy and Big Data Analytics (BDA) 17.6.3 Privacy and Blockchain Technology 17.6.4 Privacy in Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning 17.7 Transparency and Accountability Measures to Ensure Data Privacy 17.8 Conclusion References 18 Deep Learning Techniques for Social Media Analytics 18.1 Social Media and Social Media Analytics 18.1.1 What is Social Media? 18.1.2 Categories of Social Media 18.1.3 Examples of Social Media 18.1.4 The Importance of Social Media Analytics 18.2 Deep Learning 18.2.1 What is Deep Learning? 18.2.2 Types of Deep Learning Algorithms 18.3 Deep Learning Approaches for Social Media Analytics 18.3.1 Business Investigations 18.3.2 User Behavior Analysis 18.3.3 Sentiment Analysis 18.3.4 Anomaly Detection 18.4 Social Media Analytics Tools in the Market 18.4.1 Notable Free and Paid Social Media Analytics Tools 18.4.2 Social Media Analytics Dashboards from Social Media Networks 18.5 Case Study 18.5.1 How Facebook Uses Deep Learning 18.5.2 How Google Uses Deep Learning 18.5.3 How Twitter Uses Deep Learning 18.6 Conclusion References
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