Learning with Similarity Functions on Graphs using Matchings of Geometric Embeddings

By Fredrik D. Johansson, Devdatt P. Dubhashi. 2015

42 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Geometry and Topology

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Citations in the corpus

Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (2018, background): “Recent works focus on assignment-based approaches (Kriege, Giscard, and Wilson 2016; Nikolentzos, Meladianos, and Vazirgiannis 2017; Johansson and Dubhashi 2015), spectral approaches (Kondor and Pan 2016), and graph decomposition approaches (Niko-lentzos et al. 2018).”
A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications (2017, background): “Recently, some work starts to match node embedding for graph similarity [50], [153].”
On Valid Optimal Assignment Kernels and Applications to Graph Classification (2016, background): “Johansson and Dubhashi [14] derived kernels from optimal assignments by first sampling a fixed set of so-called landmarks.”
The Multiscale Laplacian Graph Kernel (2016, methodology): “Following the MRF route, given two graphs G 1 and G 2 of n vertices, we can de-ﬁne the kernel between them to be a kernel between the corresponding distributions p 1 = N (0 , L − 1 1 ) and p 2 = N (0 , L − 1 2 ) .”
A Degeneracy Framework for Graph Similarity (2018, background): “Recently, there was a surge of interest in kernels that are built upon global properties of graphs [Johansson et al. , 2014; Johansson and Dubhashi, 2015; Nikolentzos et al. , 2017b].”
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings (2019, background): “Other recent works focus on assignment-based (Johansson & Dubhashi, 2015; Kriege et al., 2016; Nikolentzos et al., 2017), spectral (Kondor & Pan, 2016; Verma & Zhang, 2017), graph decomposition (Nikolentzos et al., 2018), randomized binning approaches (Heimann et al., 2019), and the extension of…”
Matching Node Embeddings for Graph Similarity (2017, methodology): “A second approach compares pairs of graphs by computing the optimal matching between the embeddings of their vertices generated from various graph-related matrices (adjacency matrix, Laplacian matrix, etc.) (Johansson and Dubhashi 2015).”
Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs (2017, unknown): “Moreover, recently graph kernels using matchings [27] and geometric embeddings [28], [29] have been proposed.”
A survey on graph kernels (2019, methodology): “Matchings of Geometric Embeddings (Johansson and Dubhashi 2015) IM + +”