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Local Clustering of Large Graphs by Approximate Fiedler Vectors

  • Pekka Orponen
  • Satu Elisa Schaeffer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3503)

Abstract

We address the problem of determining the natural neighbourhood of a given node i in a large nonunifom network G in a way that uses only local computations, i.e. without recourse to the full adjacency matrix of G. We view the problem as that of computing potential values in a diffusive system, where node i is fixed at zero potential, and the potentials at the other nodes are then induced by the adjacency relation of G. This point of view leads to a constrained spectral clustering approach. We observe that a gradient method for computing the respective Fiedler vector values at each node can be implemented in a local manner, leading to our eventual algorithm. The algorithm is evaluated experimentally using three types of nonuniform networks: randomised “caveman graphs”, a scientific collaboration network, and a small social interaction network.

Keywords

Source Node Local Cluster Large Graph Simple Random Walk Global Cluster 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Pekka Orponen
    • 1
  • Satu Elisa Schaeffer
    • 1
  1. 1.Laboratory for Theoretical Computer ScienceTKK Helsinki University of TechnologyFinland

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