Skip to Main content Skip to Navigation
Conference papers

A Comparison of Explicit and Implicit Graph Embedding Methods for Pattern Recognition

Abstract : In recent years graph embedding has emerged as a promising solution for enabling the expressive, convenient, powerful but computa tional expensive graph based representations to benefit from mature, less expensive and efficient state of the art machine learning models of statistical pattern recognition. In this paper we present a comparison of two implicit and three explicit state of the art graph embedding methodologies. Our preliminary experimentation on different chemoinformatics datasets illustrates that the two implicit and three explicit graph embedding approaches obtain competitive performance for the problem of graph classification.
Document type :
Conference papers
Complete list of metadata

Cited literature [20 references]  Display  Hide  Download
Contributor : Benoit Gaüzère <>
Submitted on : Monday, July 8, 2013 - 4:25:33 PM
Last modification on : Thursday, June 24, 2021 - 11:58:03 AM
Long-term archiving on: : Tuesday, April 4, 2017 - 3:00:18 PM


Publisher files allowed on an open archive


  • HAL Id : hal-00829226, version 1


Donatello Conte, Jean-Yves Ramel, Nicolas Sidère, Muhammad Muzzamil Luqman, Benoit Gaüzère, et al.. A Comparison of Explicit and Implicit Graph Embedding Methods for Pattern Recognition. 9th IAPR - TC 15 workshop on Graph Based Repesentations in Pattern Recognition, May 2013, Vienne, Austria. pp.81. ⟨hal-00829226⟩



Record views


Files downloads