Computational Social Networks

The study of computational social networks (CSNs) is an emerging interdisciplinary field, concerned with the intersection of social behavior and computer systems.

Due to the dynamic and ever-evolving nature of social networks, it can be difficult to comprehend the connections and influence between users. However, visualization techniques can aid the understanding of how these networks function. This comprehensive text/reference 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: includes a thorough introduction to this exciting and blossoming field; 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.

This authoritative work is a useful resource for researchers and developers involved in social network research and related issues. Postgraduate and senior undergraduate students will also find this a valuable reference for courses on CSNs.

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.