CFP: TKDD-CASIN special issue on Computational Aspects of Social and Information Networks: Theory, Methodologies, and Applications

CFP: TKDD-CASIN: ACM Transactions on Knowledge Discovery from Data Special Issue on Computational Aspects of Social and Information Networks: Theory, Methodologies, and Applications

Deadline: September 30, 2012, Guest editor: Wei ChenJie Tang


**New** Submission website is open. The authors please select “Manuscript Type” as “CASIN special issue” when submitting the papers.


With the blessing of information technology, we are living in an increasingly networked world. People, information and other entities are connected via World Wide Web, email networks, instant messaging networks, mobile communication networks, online social networks, etc. These online networks grow fast and possess huge amount of recorded information, which presents great opportunities in understanding the science of these networks, and in developing new applications for these networks. However, new challenges have to be met --- the networks are huge and information is noisy, and they demand new methodologies in analyzing these networks, and in developing theories and applications for the networks.

To meet with these challenges, researchers across a wide range of areas in computer science, including theory and algorithms, data mining, machine learning, computer systems and networks, human-computer interface, etc. are all actively studying the computational aspects concerning social and information networks. Following the success of the International Workshop on Computational Aspects of Social and Information Networks (CASIN’2011) held in the summer of 2011 (, we would like to organize this special issue to continue fostering research and information exchange in this fast growing and inter-disciplinary area.  The special issue aims at promoting significant research results that contribute to the fundamental theories and algorithms on social and information networks, the analysis of large-scale network data, and emerging applications targeted at real-world social and information networks.

Topics of Interest

We welcome papers in all areas of social Web search and data mining, especially those that address the special theme or are inter-disciplinary in nature. Examples of relevant topics include:

  • Computational Network Theories

    • Theory of network and community formation
    • Modeling and analysis of heterogeneous networks
    • Mechanisms for information propagation
    • Social influence analysis and influence maximization
    • Topology of social and information networks
    • Inferring social ties and communities in social networks
    • Machine learning for social networks
    • Game theory and economics in social networks
    • Privacy and security in social networks
  • Methodologies and Applications

    • Large-scale network mining and analysis
    • Collective intelligence for crowdsourcing
    • Link analysis and prediction
    • Social recommendation in dynamic networks
    • Applications of social gaming
    • Personalization in social networks
    • Social media and viral marketing/advertising
    • Impact of social networks and social media on society, e.g., politics, culture, arts, etc.
    • Software tools and platforms for social media systems


On-Line Submissions:

Submission needs to go through the standard ACM TKDD submission web site. The authors please select “Manuscript Type” as “CASIN special issue” when submitting the papers.

Each paper will be peer-reviewed by at least three reviewers. Details of the journal, manuscript preparation, and recent articles are available on the website: 

Important Dates

  • Submission of full papers: September 30, 2012
  • First round notification: November 30, 2012
  • Final notification of paper acceptance: January 15, 2013, March 15th, 2013
  • Camera ready: February 28, 2013, April 5th, 2013

Guest Editors

  • Jie Tang, Tsinghua University
  • Wei Chen, Microsoft Research Asia

Contact information: Wei Chen< weic  [at]> and Jie Tang  < jietang  [at]>.

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