A social network is a social architecture made up of a set of actors (such 
as individuals or organizations) and a web of linkages between these 
actors. The social network perspective offers an interesting way of 
analyzing the structure of whole social entities. Social network analysis 
is instrumental in identifying local and global patterns, localizing 
influential entities, and examining complex network dynamics. Social 
networks are self-organizing, emergent, and complex structures with 
globally coherent patterns appearing from the local interaction of the 
elements that form the overall system.

In light of the inherent human-centric facet of social networks, the 
principles and practice of Computational Intelligence have been poised to 
play a vital role in the analysis, design, and interpretation of the 
architectures and functioning of social networks. In particular, we can 
capitalize on the important facets of learning, structural design and 
interpretability along with human-centricity, where all of these aspects 
are vigorously supported by the leading technologies of Computational 
Intelligence.

The ultimate objectives of this edited volume is to provide the reader 
with an updated, in-depth material on the conceptually appealing and 
practically sound information technology of Computational Intelligence in 
social networks analysis, synthesis and evaluation.

The volume is aimed at a broad audience of researchers and practitioners. 
It will appeal to the well-established communities including those active 
in various disciplines in which social networks, their analysis and 
optimization are of genuine relevance. Those involved in operations 
research, management, various branches of engineering, and economics will 
also benefit from the exposure to the subject matter.

Topics of interest include, but are not limited to, the following:

   a.. Graph models of social networks and their generalizations
   b.. Information granules and optimization mechanisms
   c.. Adaptive network models of social networks
   d.. Studies in learning schemes of social networks
   e.. Frameworks of decision-oriented networks
   a.. Case studies in representative areas of applications

Submission Procedure

Please submit a brief one-page summary of the proposed chapter clearly 
identifying the main objectives of your contribution by February 1, 2013. 
Authors of the accepted proposals will be notified and provided with 
detailed guidelines. Full chapters are to be submitted by May 1, 2013. All 
manuscripts will be thoroughly reviewed. The lead authors will be provided 
with a complimentary copy of the volume.

The proposals and manuscripts are to be submitted electronically to both 
editors: Professor Witold Pedrycz (wpedrycz@ualberta.ca) and Professor 
Shy-Ming Chen (smchen@mail.ntust.edu.tw)

Important Dates

February 1, 2013     Brief Proposal Submission

February 10, 2013   Notification of Acceptance

May 1, 2013           Full Chapter Submission

June 1, 2013           Review Results Returned

July 1, 2013            Final Chapter Submission