Measuring Graph Similarity Using Node Indexing and Message Passing


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

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In