Name
Abstract | ||
|---|---|---|
Graph classification is an important data mining task, and various graph kernel methods have been proposed recently for this task. These methods have proven to be effective, but they tend to have high computational overhead. In this paper, we propose an alternative approach to graph classification that is based on feature vectors constructed from different global topological attributes, as well as global label features. The main idea is that the graphs from the same class should have similar topological and label attributes. Our method is simple and easy to implement, and via a detailed comparison on real benchmark datasets, we show that our topological and label feature-based approach delivers competitive classification accuracy, with significantly better results on those datasets that have large unlabeled graph instances. Our method is also substantially faster than most other graph kernels. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012 © 2012 Wiley Periodicals, Inc. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1002/sam.11153 | Statistical Analysis and Data Mining |
Keywords | Field | DocType |
label attribute,graph kernel,graph classification,competitive classification accuracy,label feature-based approach,wiley periodicals,global label feature,large unlabeled graph instance,various graph kernel method,different global topological attribute,effective graph classification | Graph kernel,Data mining,Overhead (computing),Computer science,Graph classification,Artificial intelligence,Topology,Graph,Feature vector,Clique-width,Machine learning,Moral graph,Graph (abstract data type) | Journal |
Volume | Issue | Citations |
5 | 4 | 32 |
PageRank | References | Authors |
1.12 | 28 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Geng Li | 1 | 42 | 2.29 |
| Murat Semerci | 2 | 96 | 4.98 |
| Bülent Yener | 3 | 1075 | 94.51 |
| Mohammed Javeed Zaki | 4 | 7972 | 536.24 |