Computational Social Networks

Mining and Visualization
2012
ISBN: 978-1-4471-6237-7
Verlag: Springer

Buch, Englisch, 386 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 604 g

This book is the third of three volumes that illustrate the concept of social networks from a computational point of view. The book contains contributions from a international selection of world-class experts, with a specific focus on knowledge discovery and visualization of complex networks (the other two volumes review Tools, Perspectives, and Applications, and Security and Privacy in CSNs). Topics and features: presents the latest advances in CSNs, and illustrates how organizations can gain a competitive advantage from a better understanding of complex social networks; discusses the design and use of a wide range of computational tools and software for social network analysis; describes simulations of social networks, and the representation and analysis of social networks, highlighting methods for the data mining of CSNs; provides experience reports, survey articles, and intelligence techniques and theories relating to specific problems in network technology.

Zielgruppe

Research

Autoren/Hrsg.

Abraham, Ajith

Fachgebiete

• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Social Media, Semantic Web, Web 2.0
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining

Inhaltsverzeichnis

Part I: Mining.- Social Networks Analysis.- Performance Evaluation of Social Network Using Data Mining Techniques.- Bio-Inspired Clustering and Data Diffusion in Machine Social Networks.- Mining Geo-Referenced Community-Contributed Multimedia Data.- Correlation Mining for Web News Retrieval.- Mining Micro-Blogs.- Mining Buyer Behavior Patterns Based on Dynamic Group-Buying Networks.- Reliable Online Social Network Data Collection.- Knowledge Mining from the Twitter Social Network.- Part II: Visualization.- Mining and Visualizing Research Networks using the Artefact-Actor-Network Approach.- Intelligent Visual Pattern Clustering for Storage Layouts in Virtual Environments.- Extraction and Analysis of Facebook Friendship Relations.- Analysis of Human-Computer Interactions and Online Social Networking.- Implementation of Social Network Analysis for Web Cache Content Mining Visualization.