Learning Distances Between Graph Nodes and Edges
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-23028-8_11
Reference23 articles.
1. Algabli, S., Serratosa, F.: Embedding the node-to-node mappings to learn the graph edit distance parameters. Pattern Recogn. Lett. 112, 353–360 (2018)
2. Caetano, T.S., McAuley, J.J., Cheng, L., Le, Q.V., Smola, A.J.: Learning graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 31(6), 1048–1058 (2009)
3. Cortés, X., Conte, D., Cardot, H.: Learning edit cost estimation models for graph edit distance. Pattern Recogn. Lett. 125, 256–263 (2019)
4. Cortés, X., Conte, D., Cardot, H., Serratosa, F.: A deep neural network architecture to estimate node assignment costs for the graph edit distance. In: Proceedings of Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshop, S+SSPR 2018, Beijing, China, 17–19 August 2018, Vol. 4, pp. 326–336 (2018)
5. Garcia-Hernandez, C., Fernández, A., Serratosa, F.: Ligand-based virtual screening using graph edit distance as molecular similarity measure. J. Chem. Inf.. Model. 59 (2019)
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