@inproceedings{mautner_esann19,
author = {Mautner, Stefan and Backofen, Rolf and Costa, Fabrizio},
title = {Progress Towards Graph Optimization: Efficient Learning of Vector to Graph Space Mappings},
booktitle = {ESANN 2019 - Proceedings},
year = {2019},
doi = {},
user = {miladim},
publisher = {i6doc},
location = {Bruges, Belgium},
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.}
}