Community Structure Analysis from Social Networks

This book addresses social and complex network analysis challenges, exploring social network structures, dynamic networks, and hierarchical communities. Emphasizing network structure heterogeneity, including directionality and dynamics, it covers community structure concepts like distinctness, overlap, and hierarchy. The book aims to present challenges and innovative solutions in community structure detection, incorporating diversity into problem-solving. Furthermore, it explores the applications of identified community structures within network analysis, offering insights into social network dynamics. Investigates the practical applications and uses of community structures identified from network analysis across various domains of real-world networks Highlights the challenges encountered in analyzing community structures and presents state-of-the-art approaches designed to address these challenges Spans into various domains like business intelligence, marketing, and epidemics, examining influential node detection and crime within social networks Explores methodologies for evaluating the quality and accuracy of community detection models Examines a diverse range of challenges and offers innovative solutions in the field of detecting community structures from social networks The book is a ready reference for researchers and scholars of Computer Science and Computational Social Systems working in the area of Community Structure Analysis from Social Network Data.

Table of Contents:
Understanding and Analyzing Social Networks: Types, Dataset Analysis and Challenges 1. Deciphering Social Networks: Types, Applications, and Analytical Challenges 2. Social Network Analysis: Strategies & Challenges in Data Collection, Representation & Analysis 3. Comparative Study of Open Dataset Repositories for Community Detection and Information Diffusion in Online Social Networks Exploration of Community Detection in Social Networks 4. Community Detection: Exploring Structure and Dynamics 5. Graph Clustering Techniques for Community Detection in Social Networks 6. Semi-supervised and Deep Learning Approaches to Social Network Community Analysis Applications of Community Detection: From Biology to Social Challenges 7. Applications of Community Detection in Biological Networks 8. Influential node detection based on implicit communities 9. Connected Communities: The Role of Social Networks in Pandemic Preparedness and Mitigation 10. Identifying Spread Blockers using Overlapping Community Detection for Pandemic Management 11. Spotting Plagiarism in Academic Social Networks by Community Network Identification

About the Author :
Dr. Sajid Yousuf Bhat holds a Bachelor's and Master's degree in Computer Science from the University of Kashmir and earned his Ph.D. in Computer Science from Jamia Millia Islamia, New Delhi. He is Currently serving as a Senior Assistant Professor in the Department of Computer Science at the University of Kashmir, Hazratbal, Srinagar, Kashmir, India. He has over eight years of experience in academia and research. He has previously held faculty positions at the University of Delhi and the Central University of Kashmir. Dr. Bhat has published research articles in prestigious international journals, including IEEE Transactions, as well as in books and conference proceedings. His research interests encompass complex networks, social network analytics, community analytics, machine learning, and computer vision. Dr. Fouzia Jan is an MBA in Finance and Marketing from Bangalore University. She has done her Ph.D. in Microfinance from Jamia Millia Islamia, New Delhi. She has published many research papers in renowned journals and presented various research papers in National as well as International Journals. Besides, she was awarded Maulana Azad National Fellowship by UGC, India during her research. She has qualified National Eligibility Test with JRF conducted by UGC, India in June, 2012. At present she is an assistant professor in management at Department of Humanities and Social Sciences, National Institute of Technology, Srinagar, Kashmir. She has also worked as a management faculty at University of Kashmir and Central University of Kashmir. Her areas of research interest include Microfinance, Service Marketing, Entrepreneurship, Finance and Banking. Dr. M. Abulaish, a Professor of Computer Science at South Asian University (SAU), New Delhi, India, has over 26 years of academic and research experience. Since joining SAU in 2016, he has held key administrative roles, including Chairperson of the Computer Science Department, Director of Admissions and Examinations, and Acting Registrar. Previously, he served as Professor and Head of the Computer Science Department at Jamia Millia Islamia, New Delhi, and led a research group at King Saud University's Centre of Excellence in Information Assurance. Dr. Abulaish earned his Ph.D. from IIT Delhi in 2007 and founded the Laboratory for Data Science and Analytics at SAU, focusing on data-intensive interdisciplinary research. His work spans data mining, AI, machine learning, and network analysis, with applications in text mining, social network analysis, rumor detection, sentiment analysis, health informatics, and cybersecurity. He has authored over 140 publications, including eight in IEEE/ACM Transactions.