Abstract
Several applications can be developed when graphs represent objects composed of local parts and their relations. For instance, chemical compounds are characterised by nodes that represent chemical elements and edges that represent bonds between them. Given this representation, applications such as drug discovery (graph generation), toxicity prediction (graph regression) or drug analysis (graph classification) can be developed. In all of these applications, it is crucial to properly define how similar are the local parts and how important are them in the application at hand. We present a method that learns these similarities of local parts of the objects and also how important are when objects are represented by attributed graphs and attributes on the graphs are categorical values. Although the method is independent of the application, we have empirically tested on drug classification obtaining competitive results.
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This project has received funding from Martí-Franquès Research Fellowship Programme of Universitat Rovira i Virgili.
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Rica, E., Álvarez, S., Serratosa, F. (2022). Learning Distances Between Graph Nodes and Edges. In: Krzyzak, A., Suen, C.Y., Torsello, A., Nobile, N. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2022. Lecture Notes in Computer Science, vol 13813. Springer, Cham. https://doi.org/10.1007/978-3-031-23028-8_11
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