Gaussian Rank-Based Neighborhood Degree for Graph Neural Networks in Image Classification
The exponential growth of data has intensified the gap between the availability of unlabeled data and the high cost of manual annotation. Graph Neural Networks (GNNs) have emerged as a promising solution, as they exploit relational structures and learn from both labeled and unlabeled data, performing semi-supervised learning. A crucial component of many of these models is degree-based normalization, which influences message propagation but typically assumes uniform importance among neighboring nodes. In image classification, graphs are usually constructed from feature similarity, where treating all neighbors equally may overlook important variations in relevance. Motivated by this gap, we...
| Authors | Rafael Mendonça Duarte, Jean Roberto Ponciano, Lucas Pascotti Valem |
|---|---|
| Primary category | cs.CV |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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