Social networks research has come a long way since the notable “six-degree separation” experiment. In recent years, social network research has advanced significantly, thanks to the prevalence of the online social websites and the availability of a variety of offline large-scale social network systems such as collaboration networks. These social network systems are usually characterized by the complex network structures and rich accompanying contextual information. Researchers are increasingly interested in addressing a wide range of challenges residing in these disparate social network systems, including identifying common static topological properties and dynamic properties during the formation and evolution of these social networks, and how contextual information can help in analyzing the pertaining social networks. These issues have important implications on community discovery, anomaly detection, trend prediction and can enhance applications in multiple domains such as information retrieval, recommendation systems, security and so on.

The third SNA-KDD '2009 aims to bring together practitioners and researchers with a specific focus on the emerging trends and industry needs associated with the traditional Web, the social Web, and other forms of social networking systems. Both theoretical and experimental submissions are encouraged. The interesting topics include (1) data mining advances on the discovery and analysis of communities, on personalization for solitary activities (like search) and social activities (like discovery of potential friends), on the analysis of user behavior in open fora (like conventional sites, blogs and fora) and in commercial platforms (like e-auctions) and on the associated security and privacy-preservation challenges; (2) social network modeling, scalable, customizable social network infrastructure construction, dynamic growth and evolution patterns identification and discovery using machine learning approaches or multi-agent based simulation.