Spreading Activation Model for Connectivity Based Clustering

  • QingYuan Huang
  • JinShu Su
  • YingZhi Zeng
  • YongJun Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4243)


Connectivity based clustering has wide application in many networks like ad hoc networks, sensor networks and so on. But traditional research on this aspect is mainly based on graph theory, which needs global knowledge of the whole network. In this paper, we propose a intelligent approach called spreading activation models for connectivity based clustering (SAMCC) scheme that only local information is needed for clustering. The main feature of SAMCC scheme is applying the idea of spreading activation, which is an organization method for human long-term memory, to clustering and the whole network can be clustered in a decentralized automatic and parallel manner. The SAMCC scheme can be scaled to different networks and different level clustering. Experiment evaluations show the efficiency of our SAMCC scheme in clustering accuracy.


Neighboring Node Semantic Network Network Graph Cluster Scheme Spreading Activation 
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.
    Matula, D.W.: Graph theoretic techniques for cluster analysis algorithms. In: Classification and clustering, pp. 95–129 (1977)Google Scholar
  2. 2.
    Wu, Z., Leahy, R.: An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11), 1101–1113 (1993)CrossRefGoogle Scholar
  3. 3.
    Dongen, S.: A new cluster algorithm for graphs. Technical report, Amsterdam, The Netherlands (1998)Google Scholar
  4. 4.
    Ramaswamy, L., Gedik, B., Liu, L.: A distributed approach to node clustering in decentralized peer-to-peer networks. IEEE Trans. Parallel Distrib. Syst. 16(9), 814–829 (2005)CrossRefGoogle Scholar
  5. 5.
    Hartuv, E., Shamir, R.: A clustering algorithm based on graph connectivity. Inf. Process. Lett. 76(4-6), 175–181 (2000)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Quillian, M.R.: Semantic memory. In: Collins, A., Smith, E.E. (eds.) Readings in Cognitive Science: A Perspective from Psychology and Artificial Intelligence, pp. 80–101. Kaufmann, San Mateo (1988)Google Scholar
  7. 7.
    Crestani, F.: Retrieving documents by constrained spreading activation on automatically constructed hypertexts. In: Fifth European Congress on Intelligent Techniques and Soft Computing, Aachen, Germany, pp. 1210–1214 (1997)Google Scholar
  8. 8.
    Atkinson, R.L., Smith, E.E., Nolen-Hoeksema, S.: Introduction to Psychology. Wadsworth Publishing Company, Boston (2002)Google Scholar
  9. 9.
    Ziegler, C.N., Lausen, G.: Spreading activation models for trust propagation. In: EEE 2004. Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE 2004), Washington, DC, USA, pp. 83–97. IEEE Computer Society, Los Alamitos (2004)CrossRefGoogle Scholar
  10. 10.
    Ceglowski, M., Coburn, A., Cuadrado, J.: Semantic search of unstructured data using contextual network graphs. Technical report, Vermont, USA (2003)Google Scholar
  11. 11.
    Winick, J., Jamin, S.: Inet-3.0: Internet topology generator. Technical report, University of Michigan, USA (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • QingYuan Huang
    • 1
  • JinShu Su
    • 1
  • YingZhi Zeng
    • 1
  • YongJun Wang
    • 1
  1. 1.School of ComputerNational University of Defense TechnologyChangShaChina

Personalised recommendations