Social Network Analysis and Mining

The rapid increase in the interest in social networks has motivated the need for a more specialized venues with wider spectrum capable of meeting the needs and expectations of a variety of researchers and readers. Social Network Analysis and Mining (SNAM) is intended to be a multidisciplinary journal to serve both academia and industry as a main venue for a wide range of researchers and readers from social sciences, mathematical sciences, medical and biological sciences and computer science. We solicit experimental and theoretical work on social network analysis and mining using different techniques from sociology, social sciences, mathematics, statistics and computer science. The main areas covered by SNAM include: (1) data mining advances on the discovery and analysis of communities, personalization for solitary activities (like search) and social activities (like discovery of potential friends), the analysis of user behavior in open forums (like conventional sites, blogs and forums) and in commercial platforms (like e-auctions), and the associated security and privacy-preservation challenges; (2) social network modeling, construction of scalable, customizable social network infrastructure, identification and discovery of dynamics, growth, and evolution patterns using machine learning approaches or multi-agent based simulation. Papers should elaborate on data mining or related methods, issues associated to data preparation and pattern interpretation, both for conventional data (usage logs, query logs, document collections) and for multimedia data (pictures and their annotations, multi-channel usage data).
Topics include but are not limited to:

• Web community
• Personalization for search and for social interaction
• Recommendations for product purchase
• information acquisition and establishment of social relations
• Recommendation networks
• Data protection inside communities
• Misbehaviour detection in communities
• Preparing data for web mining
• Pattern presentation for end-users and experts
• Evolution of communities in the Web
• Community discovery in large-scale social networks
• Dynamics and evolution patterns of social networks, trend prediction
• Contextual social network analysis
• Temporal analysis on social networks topologies
• Search algorithms on social networks
• Multi-agent based social network modeling and analysis
• Large-scale graph algorithms
• Applications of social network analysis
• Anomaly detection in social network evolution

from - http://www.springer.com/computer/database+management+%26+information+retrieval/journal/13278