Detecting Similar Programs via The Weisfeiler-Leman Graph Kernel

By Wenchao Li, H. Saidi, H. Sánchez, Martin Schäf, Pascal Schweitzer. 2016

30 citations recorded by the source corpus.

Subfields: Information Systems, Software, Signal Processing

How researchers see this paper

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

In Information Systems

1. Matthias Fey
2. Jan Eric Lenssen
3. William L. Hamilton
4. Christopher Morris
5. Gaurav Rattan
6. Fredrik D. Johansson
7. Nils M. Kriege
8. Jure Leskovec

In Software

1. Christopher Morris
2. Fredrik D. Johansson
3. Nils M. Kriege
4. Gaurav Rattan
5. Matthias Fey
6. William L. Hamilton

In Signal Processing

1. Christopher Morris
2. Fredrik D. Johansson
3. Martin Grohe
4. Matthias Fey
5. William L. Hamilton

Citations in the corpus

• Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (2018, methodology): “Notable instances of this model include Neural Fingerprints (Duvenaud et al. 2015), Gated Graph Neural Networks (Li et al. 2016), GraphSAGE (Hamilton, Ying, and Leskovec 2017a), SplineCNN (Fey et al. 2018), and the spectral approaches proposed in (Bruna et al. 2014; Defferrard, X., and…”
• A survey on graph kernels (2019, methodology): “In (Li et al. 2016) the Weisfeiler-Lehman subtree kernel was used to define a similarity function for call graphs of Java programs to identify similar call graphs. de Vries (2013) extended the Weisfeiler-Lehman subtree kernel so that it can handle RDF data.”