jcst 2011 - Special Issue on Community Analysis and Information Recommendation

Journal of Computer Science and Technology
(http://jcst.ict.ac.cn)

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 11pt.
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 at http://jcst.ict.ac.cn
When submitting please select " Special Issue on Community Analysis
and Information Recommendation" in the Manuscript Type column.