Fast Random Walk Graph Kernel

By U. Kang, Hanghang Tong, I. Watson, Jimeng Sun. 2012

143 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Hardware and Architecture, Graph Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bryan Perozzi
2. Christopher Morris
3. Petra Mutzel
4. William L. Hamilton
5. Jun Zhu
6. Steven S. Skiena
7. Vivek Kulkarni
8. Jure Leskovec

In Computer Vision and Pattern Recognition

1. Christopher Morris
2. Petra Mutzel
3. Fredrik D. Johansson
4. Steven S. Skiena
5. Nils M. Kriege
6. Kristian Kersting
7. Bryan Perozzi
8. Vivek Kulkarni

In Hardware and Architecture

1. Steven S. Skiena
2. Bryan Perozzi
3. Vivek Kulkarni
4. Jun Zhu
5. Juzheng Li
6. Christopher Morris
7. Rami Al-Rfou
8. Danai Koutra

In Graph Learning

1. Bryan Perozzi
2. Jun Zhu
3. Steven S. Skiena
4. Juzheng Li
5. Vivek Kulkarni
6. Christopher Morris
7. Petra Mutzel
8. Haochen Chen

Citations in the corpus

• DeepWalk: online learning of social representations (2014, methodology): “Graph Kernels [42] have been proposed as a way to use relational data as part of the classification process, but are quite slow unless approximated [19].”
• Walklets: Multiscale Graph Embeddings for Interpretable Network Classification (2016, methodology): “Graph Kernels Graph Kernels [17] have been proposed as a way to use relational data as part of the classification process, but are quite slow unless approximated [8].”
• Graph based anomaly detection and description: a survey (2014, background): “2011a); social networks similarity (Macindoe and Richards 2010) which is based on graph features that are of value from the social viewpoint; computing edge curvatures under heat kernel embedding (Elghawalby and Hancock 2008); comparison of the number of spanning trees (Kelmans 1976); fast random wa”
• Combating Fake News (2019, background): “[120] similarly defined a random walk graph kernel [46] to calculate similarity between different propagation trees.”
• Don't Walk, Skip!: Online Learning of Multi-scale Network Embeddings (2016, methodology): “Graph Kernels Graph Kernels [19] have been proposed as a way to use relational data as part of the classification process, but are quite slow unless approximated [20].”
• Discriminative Deep Random Walk for Network Classification (2016, methodology): “, 2010) propose Graph Kernels to use relational data during classification process and (Kang et al., 2012) propose a faster approximated method of Graph Kernels.”
• 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 survey on graph kernels (2019, methodology): “For sparse graphs, O ( ln 2 ) is achieved in all cases, where a graph G is said to be sparse if | E Further improvements of the running time were subsequently achieved by non-exact algorithms based on low rank approximations (Kang et al. 2012).”