Learning Graph Distances with Message Passing Neural Networks

@article{Riba2018LearningGD,
  title={Learning Graph Distances with Message Passing Neural Networks},
  author={Pau Riba and Andreas Fischer and Josep Llad{\'o}s and Alicia Forn{\'e}s},
  journal={2018 24th International Conference on Pattern Recognition (ICPR)},
  year={2018},
  pages={2239-2244}
}
Graph representations have been widely used in pattern recognition thanks to their powerful representation formalism and rich theoretical background. A number of error-tolerant graph matching algorithms such as graph edit distance have been proposed for computing a distance between two labelled graphs. However, they typically suffer from a high computational complexity, which makes it difficult to apply these matching algorithms in a real scenario. In this paper, we propose an efficient graph… CONTINUE READING

Citations

Publications citing this paper.

References

Publications referenced by this paper.
SHOWING 1-10 OF 35 REFERENCES

Graph edit distance contest: Results and future challenges

  • Pattern Recognition Letters
  • 2017
VIEW 11 EXCERPTS
HIGHLY INFLUENTIAL

Geometric Deep Learning on Graphs and Manifolds Using Mixture Model CNNs

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017
VIEW 1 EXCERPT

Pyramidal Stochastic Graphlet Embedding for Document Pattern Classification

  • 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
  • 2017

Similar Papers

Loading similar papers…