Graph distances using graph union

By W. Wallis, P. Shoubridge, M. Kraetzl, D. Ray. 2001

163 citations recorded by the source corpus.

How researchers see this paper

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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.”
Recognition of shapes by editing their shock graphs (2004, unknown): “Hence, graph-matching approaches based on topology without using geometrical information [8], [50] are not applicable for shock graph matching. on the shock graph in the discrete domain.”
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).”