The skew spectrum of graphs

By Risi Kondor, Karsten M. Borgwardt. 2008

68 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Graph Learning

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Citations in the corpus

Covariant Compositional Networks For Learning Graphs (2018, methodology): “…artner, 2002; Borgwardt & Kriegel, 2005; Feragen et al., 2013), counting subgraphs (Shervashidze et al., 2009), spectral ideas (Vishwanathan et al., 2010), label propagation schemes with hashing (Shervashidze et al., 2011; Neumann et al., 2016), and even algebraic ideas (Kondor & Borgwardt, 2008).”
Graph Kernels (2008, neutral): “Another algebraic approach to graph kernels has appeared recently (Kondor and Borgwardt, 2008).”
The Multiscale Laplacian Graph Kernel (2016, background): “…random walks (G¨artner, 2002) through shortest paths (Borgwardt & Kriegel, 2005; Feragen et al., 2013) to Fourier transforms on the symmetric group (Kondor & Borgwardt, 2008), ultimately most graph kernels also reduce to computing a function of the two graphs that is either purely local or purely…”
Graph Capsule Convolutional Neural Networks (2018, background): “The Skew Spectrum of Graphs (Kondor & Borgwardt, 2008) based on group-theoretic approaches is another example in this category.”
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs (2017, background): “And, 2 strong Graph Spectrums: the Skew Spectrum (SGS) [20], Graphlet Spectrum (GS) [22].”
Weisfeiler-Lehman Graph Kernels (2011, methodology): “Recently proposed group theoretical approaches for representing graphs, the skew spectrum (Kondor and Borgwardt, 2008) and the graphlet spectrum (Kondor et al., 2009) can also be used for defining similarity measures on graphs that are computable in polynomial time.”