Optimal assignment kernels for attributed molecular graphs

By Holger Fröhlich, Jörg Kurt Wegner, Florian Sieker, Andreas Zell. 2005

190 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

On Valid Optimal Assignment Kernels and Applications to Graph Classification (2016, methodology): “This includes structured data like images [2, 5, 11], 3d shapes [1], chemical compounds [8] and proteins [4], which are often represented by graphs.”
Mining significant graph patterns by leap search (2008, methodology): “Application Demo (Classification): graph classifiers built on patterns discovered by LEAP could outperform the up-to-date graph kernel method (optimal assignment kernel [9]) in terms of scalability and accuracy.”
Learning with Similarity Functions on Graphs using Matchings of Geometric Embeddings (2015, methodology): “[13] used the idea of optimal assignments to propose a graph kernel, although not with geometric embeddings.”
Near-optimal Supervised Feature Selection among Frequent Subgraphs (2009, background): “Recent studies have proposed several effective graph classification methods, including substructure (or socalled fragment) based approaches [17, 8, 28], kernelbased approaches [16, 22, 12], and boosting methods [20].”
A statistical approach to rule learning (2006, result): “8 compares the presented results with those of an SVM with optimal assignment kernel (Fröhlich et al., 2005) for the yoshida and bloodbarrier data sets and, for the NCTRER data set, with those of kFOIL, an extension of FOIL incorporating an SVM in a novel evaluation function (Landwehr et al.”
A survey on graph kernels (2019, methodology): “Molecular graphs are not only a common benchmark dataset for graph kernels, but several kernels were specifically proposed for this domain, e.g., (Horváth et al. 2004; Swamidass et al. 2005; Ceroni et al. 2007; Mahé and Vert 2009; Fröhlich et al. 2005).”
Subgraph Matching Kernels for Attributed Graphs (2012, background): “…on large datasets of graphs with simple labels (cf. Table 1), it has been observed on several occasions that the prediction accuracy can be increased by annotating vertices or edges with additional attributes (see, e.g., Borg-wardt et al., 2005; Fr¨ohlich et al., 2005; Harchaoui & Bach, 2007).”
Weisfeiler-Lehman Graph Kernels (2011, background): “Another type of graph similarity measures, optimal assignment kernels (Fröhlich et al., 2005), arise from finding the best match between substructures of graphs.”