@InProceedings{PauRiba2018,
author="Pau Riba
and Andreas Fischer
and Josep Llados
and Alicia Fornes",
title="Learning Graph Distances with Message Passing Neural Networks",
booktitle="24th International Conference on Pattern Recognition",
year="2018",
pages="2239--2244",
optkeywords="*Best Paper Award*",
abstract="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   highcomputational   complexity,   which   makes   it   difficult   to   applythese  matching  algorithms  in  a  real  scenario.  In  this  paper, we  propose  an  efficient  graph  distance  based  on  the  emerging field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure and learns a  metric  with  a  siamese  network  approach.  The  performance of  the  proposed  graph  distance  is  validated  in  two  application cases,  graph  classification  and  graph  retrieval  of  handwritten words, and shows a promising performance when compared with(approximate)  graph  edit  distance  benchmarks.",
optnote="DAG; 600.097; 603.057; 601.302; 600.121",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=3168), last updated on Thu, 28 Mar 2019 10:24:49 +0100",
doi="10.1109/ICPR.2018.8545310",
file=":http://refbase.cvc.uab.es/files/RFL2018.pdf:PDF"
}