@InProceedings{mautner_esann19,
 author = {Stefan Mautner and Rolf Backofen and Fabrizio Costa},
 title = {Progress Towards Graph Optimization: Efficient Learning of Vector to Graph Space Mappings},
 booktitle = {ESANN 2019 - Proceedings},
 year = {2019},
 location = {Bruges, Belgium},
 publisher = {i6doc},
 isbn = {978-287587065-0},
 abstract = {Optimization in vector space domains is well understood. However,
in high dimensional settings or when dealing with structured data such as
sequences and graphs, optimization becomes difficult. A possible strategy is to
map graphs to vector codes and use machine learning to learn a map from codes
back to graphs. This in turn allows to employ standard optimization techniques
over vectors to optimize graphs. Here we propose an approach to invert a vector
mapping based on a combination of graph kernels and graph grammars. We evaluate
the proposed approach in an artificial setup and on real molecular graphs.},
  user =	 {miladim}
}