A New Algorithm for Error-Tolerant Subgraph Isomorphism Detection

By Bruno T. Messmer, Horst Bunke. 1998

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Citations in the corpus

Neural Architecture Search with Bayesian Optimisation and Optimal Transport (2018, background): “The above work either define the distance/kernel between vertices or assume the same vertex (layer) set [9, 20, 26, 35, 46], none of which apply in our setting.”
Substructure similarity search in graph databases (2005, background): “In computer vision and pattern recognition [17, 13, 1], graphs are used to represent complex structures such as hand-drawn symbols, 3D objects, and medical images.”
Thirty Years Of Graph Matching In Pattern Recognition (2004, unknown): “Lades93 [85], Pavlidis95[118], Wiskott97[170], Cho98[26], Duc99[39] Fisher73[51], Kittler89[80], Almohamad93[3], Christamas95[27], Pelillo95 [119], Pelillo95 [120], Gold96[61], Rangarajan96[127], Bomze97[12], Wilson97[167], Pelillo98 [121], Branca99[13], Huet99[71], Medasani99[105], Pelillo99[122, 1”
Comparing Stars: On Approximating Graph Edit Distance (2009, background): “A number of graph similarity measures therefore have been proposed [20, 8, 10, 24].”
Optimal Transport Kernels for Sequential and Parallel Neural Architecture Search (2020, background): “Previous work considers the neural network architectures as the graphs, then deﬁning various distances and kernels on graphs (Messmer & Bunke, 1998; Wallis et al., 2001; Kondor & Lafferty, 2002; Smola & Kondor, 2003; Gao et al., 2010; Ru et al., 2021).”
A survey of graph edit distance (2010, background): “Extending their idea, Messmer and Bunke [18, 19] deﬁned the subgraph edit distance by the minimum cost for all error-correcting subgraph isomorphisms, in which common subgraphs of different model graphs are represented only once and the limitation of inexact graph matching algorithms working on…”