Graph Embedding in Vector Spaces by Means of Prototype Selection
By Kaspar Riesen, Michel Neuhaus, Horst Bunke. 2007
134 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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
- Kaspar Riesen
- Horst Bunke
- Ehsan Zare Borzeshi
- Massimo Piccardi
- Yizhou Sun
- Wei Wang
- Haoyang Ding
- Yunsheng Bai
In Computer Vision and Pattern Recognition
- Kaspar Riesen
- Horst Bunke
- Massimo Piccardi
- Ehsan Zare Borzeshi
- Xiaogang Wang
- Yizhou Sun
- Wei Wang
- Haoyang Ding
Citations in the corpus
- Approximate graph edit distance computation by means of bipartite graph matching (2009, methodology): “There are various approaches to graph classification that make use of graph edit distance in some form, including vector space embedding classifiers [25] and graph kernels [26].”
- Discriminative prototype selection methods for graph embedding (2013, background): “Given that the vertices and/or the edges are labelled, these graphs are referred to as attributed graphs.”