Node Classification On Pascalvoc Sp 1
المقاييس
macro F1
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
جدول المقارنة
| اسم النموذج | macro F1 |
|---|---|
| next-level-message-passing-with-hierarchical | 0.4604±0.0059 |
| recipe-for-a-general-powerful-scalable-graph | 0.3748±0.0109 |
| long-range-graph-benchmark | 0.2873±0.0219 |
| where-did-the-gap-go-reassessing-the-long | 0.2718±0.0054 |
| learning-long-range-dependencies-on-graphs | 0.4912 ± 0.0042 |
| long-range-graph-benchmark | 0.3216±0.0027 |
| long-range-graph-benchmark | 0.1268±0.0060 |
| long-range-graph-benchmark | 0.2694±0.0098 |
| exphormer-sparse-transformers-for-graphs | 0.396±0.0027 |
| long-range-graph-benchmark | 0.1265±0.0076 |
| where-did-the-gap-go-reassessing-the-long | 0.2078±0.0031 |
| where-did-the-gap-go-reassessing-the-long | 0.4440±0.0065 |
| on-the-connection-between-mpnn-and-graph | 0.3103±0.0068 |
| long-range-graph-benchmark | 0.3230±0.0039 |
| unlocking-the-potential-of-classic-gnns-for | 0.4263 ± 0.0057 |
| drew-dynamically-rewired-message-passing-with | 0.3314±0.0024 |
| simple-and-deep-graph-convolutional-networks-1 | 0.1698±0.0080 |
| long-range-graph-benchmark | 0.2860±0.0085 |
| where-did-the-gap-go-reassessing-the-long | 0.3880±0.0040 |