Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph Classification
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 convo...
Ausführliche Beschreibung
Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 44(2022), 2 vom: 15. Feb., Seite 783-798
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| 1. Verfasser: |
Bai, Lu
(VerfasserIn) |
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| Weitere Verfasser: |
Cui, Lixin,
Jiao, Yuhang,
Rossi, Luca,
Hancock, Edwin R |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2022
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence
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| Schlagworte: | Journal Article |
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