DMCCI 2011
ICDM 2011 Workshop on
Data Mining Technologies for Computational Collective Intelligence
1. Overview
Computational collective intelligence aims to explore the group intelligence using
computational methods. Over the recent years, the ubiquitous use of world wide web and rapid
development of internet technology have provided an unprecedented environment of various
group activities. Numerous interdisciplinary and interdependent systems are created and used
to represent the various biological, social, physical and ecological systems for investigating the
interactions between individuals, groups, communities. This requires joint efforts to take
advantage of the state-of-the-art research from multiple disciplines develop novel theories,
experiments, and methodologies to study these rich interactions as well as make better group
decisions.
This workshop will bring together the interdisciplinary researchers from sociology, behavioral
science, computer science, psychology, bioinformatics, ecology, cultural study, information
systems, operations research to share, exchange, learn, and develop preliminary results, new
concepts, ideas, principles, and methodologies on applying data mining technologies for
computational collective intelligence, aiming to merge the gap between the two areas,
encourage collaborations, advance and deepen our understanding of interactions as well as
collective intelligence, and devise more effective and efficient computational algorithms to
make wiser decisions.
2. Topic of Interests
The topics of this special issue include, but not limit to, the following:

Graph and Matrix methods for computational collective science

Probabilistic models for collective social science

Tensor models for evolving group/community analysis

Transfer learning on heterogeneous groups/communities

Link analysis and network structure discovery

Viral Marketing and influence propagation

User behavior modeling

Social tagging, blog and forum analysis

Security and Privacy issues

Query log and click through data analysis

Expertise and authority discovery

Social navigation and visualization

Collaborative filtering and recommendation
3. Organizers
General Co-Chairs

Charu Aggarwal. IBM T.J. Watson Research Center

Tina Eliassi-Rad. Rutgers University.

Philip S. Yu. University of Illinois at Chicago
Program Co-Chairs

Hanghang Tong. IBM T.J. Watson Research Center

Fei Wang. IBM T.J. Watson Research Center
Program Committee
TBA