Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Mtandao wa Mawasiliano wa Kina wenye Usimamizi dhaifu (WS-GNN)× | Mtandao wa Neti Nyingi za Grafu× | |
|---|---|---|
| Nyanja≠ | Ujifunzaji wa Kina | Uchanganuzi wa Mitandao |
| Familia≠ | Machine learning | Process / pipeline |
| Mwaka wa asili≠ | 2017–2019 | 2017–2018 (major variants) |
| Mwanzilishi≠ | Derived from GNN literature (Scarselli et al. 2009; Kipf & Welling 2017) combined with weak supervision paradigm | — |
| Aina≠ | Graph-based deep learning with imperfect supervision | Deep learning on graph-structured data |
| Chanzo asilia≠ | Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR 2017). link ↗ | Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR). DOI ↗ |
| Majina mbadala≠ | WS-GNN, graph neural network with weak supervision, noisy-label GNN, partially supervised GNN | GNN, GCN, GAT, GraphSAGE |
| Zinazohusiana≠ | 6 | 5 |
| Muhtasari≠ | A Weakly Supervised Graph Neural Network (WS-GNN) is a graph deep-learning approach that learns from graph-structured data — nodes, edges, and their attributes — when only noisy, partial, or indirectly obtained labels are available. By coupling GNN message passing with noise-robust training strategies, it extends graph learning to real-world settings where clean, fully annotated graphs are scarce or expensive to obtain. | A Graph Neural Network (GNN) is a deep learning architecture that operates directly on graph-structured data by combining node features with structural information through iterative neighborhood message passing. The three canonical variants — the Graph Convolutional Network (GCN) introduced by Kipf and Welling in 2017, the Graph Attention Network (GAT) introduced by Veličković et al. in 2018, and GraphSAGE — differ in how they aggregate neighbor information: GCN applies a spectral convolution over the full adjacency, GAT weights neighbors by learned attention scores, and GraphSAGE samples and aggregates local neighborhoods inductively, enabling generalization to unseen nodes. |
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