Computational Models for Social Network Analysis

• Record Nr.: UNINA9910985641303321
• Autore: Kumar Ravi Ranjan
• Pubbl/distr/stampa: Burlington : , : Arcler Education Inc, , 2024; ©2024
• ISBN: 9781779561169; 1779561164
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (401 pages)
• Soggetti: Social networks; Modeling
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Cover -- Half Title -- Title Page -- Copyright -- About The Author -- Table of Contents -- List of Figures -- List of Abbreviations -- Preface -- Chapter 1: Introduction To Social Network Analysis -- Contents -- 1.1. Theoretical Foundations Of Social Network Analysis -- 1.2. Key Concepts In Social Network Analysis -- 1.3. Importance Of Computational Models In Social Network Analysis -- 1.4. Challenges And Limitations Of Social Network Analysis -- 1.5. Ethical Considerations In Social Network Analysis -- Summary -- Chapter 1 Review Questions -- Chapter 2: Network Data Collection And Representation -- Contents -- 2.1. Data Collection Methods For Social Network Analysis -- 2.2. Network Data Types And Formats -- 2.3. Data Preprocessing And Cleaning For Social Network Analysis -- 2.4. Visualization Techniques For Network Data -- 2.5. Network Metrics And Descriptive Statistics -- Chapter 2 Summary -- Chapter 2 Review Questions -- Chapter 3: Centrality And Influence Measures -- Contents -- 3.1. Degree Centrality And Its Applications -- 3.2. Betweenness Centrality And Its Significance -- 3.3. Closeness Centrality And Its Implications -- 3.4. Eigenvector Centrality And Its Role In Identifying Influencers -- 3.5. Pagerank Algorithm And Its Application To Social Networks -- Chapter 3 Summary -- Chapter 3 Review Questions -- Chapter 4: Community Detection And Analysis -- ; Contents -- 4.1. Overview Of Community Detection In Social Networks -- References -- 4.2. Modularity-based Community Detection Methods -- 4.3. Hierarchical Clustering Approaches For Community Detection -- 4.4. Spectral Clustering Techniques In Community Detection -- References -- 4.5. Evaluation Measures For Community Detection Algorithms -- References -- Chapter 4 Summary -- Chapter 4 Review Questions -- Chapter 5: Diffusion And Information Spread In Social Networks -- Contents.; 5.1. Information Diffusion Models In Social Networks -- 5.2. Epidemic Models For Information Spread -- References -- 5.3. Influence Maximization And Viral Marketing Strategies -- References -- 5.4. Cascading Behavior And Contagion Dynamics -- References -- 5.5. Modeling And Analyzing Rumor Propagation In Social Networks -- References -- Chapter 5 Summary -- Chapter 5 Review Questions -- Chapter 6: Opinion Mining And Sentiment Analysis -- Contents -- 6.1. Sentiment Analysis Techniques For Social Network Data -- References -- 6.2. Opinion Mining In Social Media -- References -- 6.3. Aspect-based Sentiment Analysis In Social Networks -- References -- 6.4. Emotion Detection And Analysis In Online Social Interactions -- References -- 6.5. Sentiment Classification And Prediction Models -- References -- Chapter 6 Summary -- Chapter 6 Review Questions -- Chapter 7: Link Prediction And Recommender Systems -- Contents -- 7.1. Link Prediction Techniques In Social Networks -- References -- 7.2. Collaborative Filtering For Recommender Systems -- References -- 7.3. Content-based Filtering Methods For Recommender Systems -- References -- 7.4. Hybrid Approaches For Link Prediction And Recommender Systems -- References -- 7.5. Evaluation Metrics For Link Prediction And Recommender Systems -- References -- Chapter 7 Summary -- Chapter 7 Review Questions -- Chapter 8: Social Network Simulation And Modeling -- Contents -- 8.1. Agent-based Models For Social Networks -- References -- 8.2. Random Graph Models For Social Network Generation -- References -- 8.3. Dynamic Network Models And Temporal Analysis -- References -- 8.4. Simulation Of Social Influence And Behavior Diffusion -- References -- 8.5. Validation And Calibration Of Social Network Models -- References -- Chapter 8 Summary -- Chapter 8 Review Questions -- Concluding Remarks.; Summative Test Questions And Answers -- Bibliography -- Index -- Back Cover.
• Sommario/riassunto: This book, 'Models for Social Network Analysis' by Dr. Ravi Ranjan Kumar, provides a comprehensive guide to understanding computational models used in social network analysis. It covers fundamental concepts, methods, and applications, offering insights into network data collection, centrality and influence measures, community detection, diffusion of information, opinion mining, link prediction, recommender systems, and social network simulation. Aimed at undergraduate students, policymakers, and practitioners in marketing, public health, and social media, the book equips readers with the knowledge to analyze social networks computationally. It highlights the importance of computational models in informing decisions, designing strategies, and addressing societal challenges. It also discusses ethical considerations such as privacy and data protection. This work serves as an introduction to the theories and practical applications of social network analysis and encourages further exploration of the field.