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Local Patterns and Supergraph for Chemical Graph Classification with Convolutional Networks

  • Évariste DallerEmail author
  • Sébastien Bougleux
  • Luc Brun
  • Olivier Lézoray
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11004)

Abstract

Convolutional neural networks (CNN) have deeply impacted the field of machine learning. These networks, designed to process objects with a fixed topology, can readily be applied to images, videos and sounds but cannot be easily extended to structures with an arbitrary topology such as graphs. Examples of applications of machine learning to graphs include the prediction of the properties molecular graphs, or the classification of 3D meshes. Within the chemical graphs framework, we propose a method to extend networks based on a fixed topology to input graphs with an arbitrary topology. We also propose an enriched feature vector attached to each node of a chemical graph and a new layer interfacing graphs with arbitrary topologies with a full connected layer.

Keywords

Graph-CNNs Graph classification Graph edit distance 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Normandie Univ, UNICAEN, ENSICAEN, CNRS, GREYCCaenFrance

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