Fuzzy multilevel graph embedding

Muhammad Muzzamil Luqman, Jean Yves Ramel, Josep Lladós, Thierry Brouard

Research output: Contribution to journalArticleResearchpeer-review

49 Citations (Scopus)


Structural pattern recognition approaches offer the most expressive, convenient, powerful but computational expensive representations of underlying relational information. To benefit from mature, less expensive and efficient state-of-the-art machine learning models of statistical pattern recognition they must be mapped to a low-dimensional vector space. Our method of explicit graph embedding bridges the gap between structural and statistical pattern recognition. We extract the topological, structural and attribute information from a graph and encode numeric details by fuzzy histograms and symbolic details by crisp histograms. The histograms are concatenated to achieve a simple and straightforward embedding of graph into a low-dimensional numeric feature vector. Experimentation on standard public graph datasets shows that our method outperforms the state-of-the-art methods of graph embedding for richly attributed graphs. © 2012 Elsevier Ltd.
Original languageEnglish
Pages (from-to)551-565
JournalPattern Recognition
Issue number2
Publication statusPublished - 1 Feb 2013


  • Explicit graph embedding
  • Fuzzy logic
  • Graph classification
  • Graph clustering
  • Graphics recognition
  • Pattern recognition


Dive into the research topics of 'Fuzzy multilevel graph embedding'. Together they form a unique fingerprint.

Cite this