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Semi-supervised Graph Neural Network/Ushahidi
Rekodi ya ushahidi wa mbinu

Semi-supervised Graph Neural Network

A semi-supervised graph neural network trains a GNN on a graph where only a small fraction of nodes carry labels, using neighborhood message-passing to spread information from labeled nodes to unlabeled ones. The approach, popularised by Kipf and Welling's 2017 Graph Convolutional Network, achieves strong node-classification accuracy even when labeled examples are scarce.

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Semi-supervised Graph Neural Network (GNN with Label Propagation)
Rekodi ya mbinu ya kiajenda · ml-model / deep-learning
  • Kipf, T. N., & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. International Conference on Learning Representations (ICLR 2017). · URL
  • Zhou, D., Bousquet, O., Lal, T. N., Weston, J., & Scholkopf, B. (2004). Learning with Local and Global Consistency. Advances in Neural Information Processing Systems (NeurIPS 2004), 17. · URL
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Same method familyGraph Convolutional Networkmachine-suggested · Relational suggestion, not evidence.See alsoGraph Neural Network (Network Analysis)machine-suggested · Relational suggestion, not evidence.Same method familyLabel Propagationmachine-suggested · Relational suggestion, not evidence.Same method familySemi-supervised Learningmachine-suggested · Relational suggestion, not evidence.

Hali ya ushahidi

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

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