Asymmetric 2-Mode Network in Social Computing and Decomposition Algorithm

  • Shuren Zhang
  • Yu Chen
  • Meiqi Fang


In Social Network Service (SNS) using Web2.0 technologies, new social structures are constructed bottom-up. Such social emergence has recently drawn many interests. New social structures are characterized by the social relation formed from the gradual development among the users in the system. In this world, the optimization of the social structure and the organization of the information in Web2.0 communities can be improved with complex network decomposition, on-line social network analysis, and other methods. Such a cross field covering man-machine interaction design, complex network computing and social network analysis is known as social computing. An important step in social computing is to decompose various 2-Mode networks in a community. On the basis that the actions of subject nodes and object nodes during social computing were analyzed and found to be different extended investigation was conducted on the 2-Mode network decomposition algorithm in this study, concluding with the proposition of several 2-Mode network decomposition algorithms.


Intermediate Node Decomposition Algorithm Social Network Service Correlation Weight Social Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Barry Wellman. For a Social network analysis of computer networks: A Socio-logical Perspective on Collaborative Work and Virtual Community. ACM SIGCPR/ SIGMIS 1996/04Google Scholar
  2. 2.
    Yang, C., H. Chen, et al. Dispositional Factors in the Use of Social Networking Sites: Findings and Implications for Social Computing Research. Intelligence and Security Informatics, Springer Berlin / Heidelberg. 5075: 392-400.Google Scholar
  3. 3.
    Ogawa, S., F.T. Piller. 2006. Reducing the Risks of New Product Development. MIT Sloan Management Review 47(2) 65-71.Google Scholar
  4. 4.
    White, D. R., J. Owen-Smith, et al. (2004). “Networks, Fields and Organizations: Micro- Dynamics, Scale and Cohesive Embeddings.” Computational & Mathematical Organization Theory 10(1): 95-117.Google Scholar
  5. 5.
    Grujic, J.; Mitrovic, M.; Tadic, B. Mixing patterns and communities on bipartite graphs on web-based social interactions, Digital Signal Processing, 2009 16th International Conference DOI: 10.1109/ICDSP.2009.5201238
  6. 6.
    Cormode, G., D. Srivastava, et al. “Anonymizing bipartite graph data using safe groupings.” The VLDB Journal 19(1): 115-139.Google Scholar
  7. 7.
    Matjaz Zaversnik. Vladimir Batagelj, Andrej Mrvar. Analysis and visualization of 2-Mode network [EB/OL] Tagungen/Ossiach/Zaversnik.pdf.2011.3.
  8. 8.
    Zhang Shuren and Fang Meiqi. Social software and complex adaptability in-formation system paradigm. Memoir from the first session of national conference of China Association of Information System (CNAIS2005,In Chinese), Tsinghua University PressGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Alibaba Business College,Hangzhou Normal UniversityHangzhouChina
  2. 2.Information College, Renmin UniversityBeijingChina

Personalised recommendations