Data Mining for Social Network Data

Driven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science. This proposed Special Issue on Data Mining for Social Network Data will present a broad range of recent studies in social networking analysis. It will focus on emerging trends and needs in discovery and analysis of communities, solitary and social activities, activities in open for a and commercial sites as well. It will also look at network modeling, infrastructure construction, dynamic growth and evolution pattern discovery using machine learning approaches and multi-agent based simulations. Editors are three rising stars in world of data mining, knowledge discovery, social network analysis, and information infrastructures, and are anchored by Springer author/editor Hsinchun Chen (Terrorism Informatics; Medical Informatics; Digital Government), who is one of the most prominent intelligence analysis and data mining experts in the world.

Social Network Data Mining: Research Questions, Techniques, and Applications.- Automatic Expansion of a Social Network Using Sentiment Analysis.- Automatic Mapping of Social Networks of Actors from Text Corpora: Time Series Analysis.- A Social Network-Based Recommender System (SNRS).- Network Analysis of US Air Transportation Network.- Identifying High-Status Nodes in Knowledge Networks.- Modularity for Bipartite Networks.- ONDOCS: Ordering Nodes to Detect Overlapping Community Structure.- Framework for Fast Identification of Community Structures in Large-Scale Social Networks.- Geographically Organized Small Communities and the Hardness of Clustering Social Networks.- Integrating Genetic Algorithms and Fuzzy Logic for Web Structure Optimization.