Haizheng Zhang

Workshop 12: The 4th SNA-KDD Workshop on Social Network Mining and Analysis (SNAKDD 2010)
July 25 2:00PM
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 fourth SNA-KDD '2010 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.



Topics


  • Communities discovery and analysis in large scale online and offline social networks
  • Personalization for search and for social interaction
  • Recommendations for product purchase, information acquisition and establishment of social relations
  • Data protection inside communities
  • Misbehavior detection in communities
  • Web mining algorithms for clickstreams, documents and search streams
  • Preparing data for web mining
  • Pattern presentation for end-users and experts
  • Evolution of patterns in the Web
  • Evolution of communities in the Web
  • 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
  • Application of social network analysis
  • Anomaly detection in social network evolution