Community Structure Analysis from Social NetworksКНИГИ » ПРОГРАММИНГ
Название: Community Structure Analysis from Social Networks Автор: Sajid Yousuf Bhat, Fouzia Jan, Muhammad Abulaish Издательство: CRC Press Год: 2026 Страниц: 256 Язык: английский Формат: epub (true) Размер: 10.1 MB
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.
The book is organized into three sections. The first section focuses on the foundational elements of social network analysis, exploring dataset characteristics, challenges in data representation, and comparisons of open dataset repositories. It also highlights various strategies for analyzing and interpreting social networks, providing insights into their types, applications, and analytical challenges.
The second section focuses on methodologies and techniques for community detection. It delves into graph clustering, semi-supervised learning, and Deep Learning approaches, providing readers with an in-depth understanding of structure and dynamics. These chapters also offer a comparative perspective on classical and modern techniques, shedding light on their strengths and applications.
The final section emphasizes the practical applications of community detection. From identifying influential nodes and detecting implicit communities to managing pandemics and detecting plagiarism, the book underscores the versatility of community structure analysis in solving real-world challenges. It also explores the role of connected communities in pandemic preparedness and the use of overlapping community detection to identify spread blockers.
Advancements in Artificial Intelligence (AI) and Machine Learning are significantly transforming of social network analysis (SNA) by streamlining data collection, network visualization, and pattern recognition, thus improving the scalability and efficiency of network analysis. Machine learning models offer predictive insights into network dynamics, facilitating real-time monitoring and forecasting of social interactions. Additionally, data mining and natural language processing (NLP) technologies allow researchers to extract valuable insights from unstructured data sources, such as social media posts and online forums, thereby enhancing the analysis with new data dimensions.
Python is a popular programming language for community detection because of its wide variety of libraries, simple syntax, and ease of integration with other computer languages. CDlib ((C)ommunity (D)iscovery Library) is a Python library used for identifying and extracting communities from networks. The library helps in community discovery analysis. CDlib is a Python package built upon the network facilities offered by networkx and igraph. The library provides a standardized input/output for several Community Detection algorithms as mentioned by Rossetti, Milli, and Cazabet. A few popular sub-libraries have been discussed below.
- 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.
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