Name
Title | ||
|---|---|---|
Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification |
Abstract | ||
|---|---|---|
In this paper, we develop a novel backtrackless aligned-spatial graph convolutional network (BASGCN) model to learn effective features for graph classification. Our idea is to transform arbitrary-sized graphs into fixed-sized backtrackless aligned grid structures and define a new spatial graph convolution operation associated with the grid structures. We show that the proposed BASGCN model not onl... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TPAMI.2020.3011866 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | DocType | Volume |
Convolution,Adaptation models,Transforms,Convolutional neural networks,Standards,Feature extraction,Kernel | Journal | 44 |
Issue | ISSN | Citations |
2 | 0162-8828 | 1 |
PageRank | References | Authors |
0.35 | 42 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lu Bai | 1 | 245 | 27.51 |
| Lixin Cui | 2 | 3 | 2.74 |
| Yuhang Jiao | 3 | 5 | 3.78 |
| Luca Rossi | 4 | 18 | 8.41 |
| Edwin R. Hancock | 5 | 5432 | 462.92 |