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Measuring Graph Similarity Using Node Indexing and Message Passing

Excerpt

In this paper, we present a generative model to measure graph similarity. The parameters of that random process generating the observed graph from a template are determined by the node indices, reflecting the structure compatibility between nodes. We propose to use the steady state of the dynamic system represented by a directed graph to index the nodes. A message passing algorithm using the loopy belief propagation is adopted to approximate the maximum a posteriori assignment (MAP) for the nodes. The resulted believes of self matching form a similarity measure of the nodes in a graph. Examples and experiments demonstrated that the proposed method worked well for large graphs.

  • Abstract
  • Key Words
  • 1 Introduction
  • 2. Related Work
  • 3 Proposed Algorithms
  • 4.Empirical Evaluation
  • 5.Conclusions
  • Acknowledgments
  • References

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