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Showing 1 to 3 of 3 for “"Graph Kernel"”.
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Graph kernel extensions and experiments with application to molecule classification, lead hopping and multiple targets
… drugs. In particular, modern structured kernel methods have been successfully applied to range of problem domains and have been recently adapted for graph structures making them directly applicable to pharmaceutical drug discovery. Specifically graph structures have a natural fit with …
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G-hash: Towards Fast Kernel-based Similarity Search in Large Graph Databases
Structured data such as graphs and networks have posed significant challenges to fundamental aspects of data management including efficient storage, indexing, and similarity search. With the fast accumulation of graph databases, similarity search in graph databases has emerged as an important …
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Information overload in structured data
… in two separate structured domains, namely, graphs and text.</p> <p>Graph kernels have been proposed as an efficient and theoretically sound approach to compute graph similarity. They decompose graphs into certain sub-structures, such as subtrees, or subgraphs. However, existing graph kernels …