@InProceedings{CI-Daller2018b,


author = {'Evariste Daller and Sébastien Bougleux and Luc Brun and Olivier L'ezoray},
title = {Local Patterns and Supergraph for Chemical Graph Classification with Convolutional Networks},
booktitle = {Proceedins of Structural, Syntactic, and Statistical Pattern Recognition(SSPR)'2018},
year = 2018,
month = {August},
address = {Beijing},
pages="97--106",
editor="Bai, Xiao
and Hancock, Edwin R.
and Ho, Tin Kam
and Wilson, Richard C.
and Biggio, Battista
and Robles-Kelly, Antonio",
organization = {IAPR},
publisher = {Springer International Publishing},
theme={pattern,ged},
url={HAL:=https://hal-normandie-univ.archives-ouvertes.fr/hal-01865180, HAL(PDF):=https://hal-normandie-univ.archives-ouvertes.fr/hal-01865180/file/sspr-2018.pdf},
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. }

 }