Halting in Random Walk Kernels
By Masashi Sugiyama, Karsten M. Borgwardt. 2015
115 citations recorded by the source corpus.
Subfields: Graph Learning, Statistical Learning Theory
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Graph Learning
- Christopher Morris
- Jure Leskovec
- Rex Ying
- Jiaxuan You
- Petra Mutzel
- William L. Hamilton
- Muhan Zhang
- Yixin Chen
In Statistical Learning Theory
- Christopher Morris
- Fredrik D. Johansson
- Jiaxuan You
- Michalis Vazirgiannis
- Jure Leskovec
- Petra Mutzel
- Kristian Kersting
- Nils M. Kriege
Citations in the corpus
- Position-aware Graph Neural Networks (2019, background): “G ¨ artner et al. (2003) and Kashima et al. (2003) also proposed graph kernels based on random walks, which count the number of walks two graphs have in common (Sugiyama & Borgwardt, 2015).”
- Anonymous Walk Embeddings (2018, methodology): “5 and kR is k -step random walk kernel with k = 3 (Sugiyama & Borgwardt, 2015).”
- Link Prediction Based on Graph Neural Networks (2018, background): “Common of substructures include walks [54, 55], subgraphs [56, 57], paths [58], and subtrees [34, 59].”
- TUDataset: A collection of benchmark datasets for learning with graphs (2020, background): “Important approaches include random-walk and shortest paths based kernels (Gärtner et al., 2003; Sugiyama & Borgwardt, 2015; Borgwardt & Kriegel, 2005; Kriege et al., 2019), as well as the Weisfeiler-Lehman subtree kernel (Shervashidze et al., 2011; Morris et al., 2017).”
- Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs (2017, background): “Since then, random walk kernels have been studied intensively [16], [20]–[23].”
- A Degeneracy Framework for Graph Similarity (2018, background): “Specifi-cally, there are kernels that compare graphs based on random walks [G¨artner et al. , 2003; Vishwanathan et al. , 2010; Sugiyama and Borgwardt, 2015], subtrees [G¨artner et al. , 2003; Mah´e and Vert, 2009], cycles [Horv´ath et al. , 2004], shortest paths [Borgwardt and Kriegel, 2005], and…”
- A survey on graph kernels (2019, methodology): “Sugiyama and Borgwardt [41] observed that applying a Gaussian RBF kernel to vertex and edge label histograms leads to a clear improvement over linear kernels.”