Near-optimal Supervised Feature Selection among Frequent Subgraphs
By Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alex Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt. 2009
121 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems
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
- Shirui Pan
- Chengqi Zhang
- Philip S. Yu
- Michalis Vazirgiannis
- François Rousseau
- Wei Wang
- Jia Wu
- Xingquan Zhu
In Computer Vision and Pattern Recognition
- Shirui Pan
- Chengqi Zhang
- Wei Wang
- Xingquan Zhu
- Michalis Vazirgiannis
- Emmanouil Kiagias
- François Rousseau
- Jia Wu
In Information Systems
- Chengqi Zhang
- Xingquan Zhu
- Philip S. Yu
- Michalis Vazirgiannis
- Emmanouil Kiagias
- François Rousseau
- Shirui Pan
- Jia Wu
Citations in the corpus
- Text Categorization as a Graph Classification Problem (2015, background): “However, when experimenting, gBoost did not converge on our larger datasets while GAIA and CORK consider subgraphs of node size at least 2, which exclude unigrams, resulting in poorer performances.”
- GAIA: graph classification using evolutionary computation (2010, background): “CORK [Thoma, 2009] proposes to use correspondence to measure the discrimination power of subgraph patterns and thereby achieves a theoretically near-optimal solution.”
- Multiple Structure-View Learning for Graph Classification (2018, methodology): “Supervised subgraph feature extraction methods have also been proposed to find discriminative subgraph features for different classes, such as LEAP [20], gPLS [21], COPK [22], and GAIA [23].”