Graphlet-based measures are suitable for biological network comparison
By Wayne B. Hayes, Kai Sun, Natasa Przulj. 2013
89 citations recorded by the source corpus.
Subfields: Experimental and Cognitive Psychology, Molecular Biology, Statistical and Nonlinear Physics, 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 Experimental and Cognitive Psychology
In Molecular Biology
- Marinka Zitnik
- Jure Leskovec
- Jaka Demsar
- Tomaž Hočevar
- William L. Hamilton
- Petar Velickovic
- Rex Ying
- Jiaxuan You
In Statistical and Nonlinear Physics
- Marinka Zitnik
- Jure Leskovec
- Jaka Demsar
- Tomaž Hočevar
- Jaewon Yang
- Manuel Gomez-Rodriguez
- Rok Sosic
- Bernhard Schölkopf
In Graph Learning
- Marinka Zitnik
- Jure Leskovec
- Jaka Demsar
- Tomaž Hočevar
- William L. Hamilton
- Stephan Günnemann
- Rex Ying
- Jian Tang
Citations in the corpus
- Predicting multicellular function through multi-layer tissue networks (2017, methodology): “Graphlet-based methods, such as graphlet degree vectors (Hayes et al. , 2013), can thus be seen as an alternative approach for extracting feature representations for nodes.”
- A combinatorial approach to graphlet counting (2014, methodology): “Using a number of large PPI networks, Hayes et al. (2013) further showed that although the network structure may be unstable in regions with low edge-density, high-density regions are suitable for network comparison using graphlet degree distributions.”