Journal of Computer Science and Technology

Special Issue on Community Analysis and Information Recommendation

Call for Papers

Over the last few years, we have witnessed a tremendous growth in online social network services (SNS), such as Facebook, Twitter, and Del.icio.us, which greatly facilitate the collaboration, sharing, and other interactions
between individuals. The term Web 2.0 has been coined to embrace all those new collaborative services or applications and also to indicate a new “social” approach to generating and distributing Web content, characterized by open communication, decentralization of authority, personalized information seeking or filtering,
and wisdom of crowd. On one hand, the ever-increasing social network services provide large-scale social data for the analysis of user behavior, and then improving the existing services. On the other hand, they also bring forth
more severe information overload and require more flexible and robust computing platforms or service paradigms.

Generally, social network services mainly focus on the two kinds of user needs, social needs and information needs. Community is the typical outcome of users’ social need and has practically become the brands of social network services. The research on community has become an important focus of social network analysis. As
the prevalence of SNS, numerous so-called user-generated-content (UGC) are available at the unprecedented level. UGC provides data for the analysis and understanding for users’behavior and thus is valuable to the
improvement of existing services. This brings forth a great deal of research issues, including query log analysis, folksonomy, and so on. As to users’ information needs, information overload poses a big challenge to the success of SNS. As an important approach for information filtering, recommendation is usually adopted in industrial and academia. Differently from traditional recommendation, social and economic factors play a critical role in the social network services. Another emerging direction for SNS is mobile SNS, which transform the platform from Internet to mobile environments. Finally, the enabling computing platform, e.g., data centers, plays an essential role in the success of SNS. For example, Facebook will have 40TB new data added to their database every week, and these data are highly shared among users, how to build a reliable and efficient data center to support the fast growing of data and population is not only an interesting but also a challenge faced by the
community. This special issue shall concentrate on recent research in these areas and thus solicits research
articles on community analysis and information recommendation, including but not limited to:

• Evolution and Dynamics of information network and social network
• Temporal and community evolution
• Social network analysis and community analysis
• Social Network Services and Social-Oriented Computing
• Mobile social network services
• Social reputation and recommendation systems
• Context-aware and multi-dimensional recommendation techniques
• Evaluation of recommendation approaches
• Innovative recommendation applications

Guest Editors:
Prof. Xue-Qi Cheng, Chinese Academy of Science, China
Prof. Weisong Shi, Wayne State University, USA
Prof. Tao Zhou, UESTC, China

Important Dates
Submission due: Oct 1, 2010
First round of reviews due: Jan 1, 2011
Revision and second round review: Apr 1, 2011
Final manuscripts ready for JCST: May/June 2011

Submission
Manuscripts should describe original research that has not yet been published or currently under review by other journals or conferences. Papers must be in single-column format, double-spaced, and use fonts no smaller than 11 pt. There is no page limit, but it is encouraged to keep the length less than 25 pages. Manuscripts must be submitted through the JCST electronic submission system. When submitting please select "Special Issue on Community Analysis and Information Recommendation" in the Areas concerned column.

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