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Found 4 papers, 2 papers with code
Not All Negatives Are Worth Attending to: Meta-Bootstrapping Negative Sampling Framework for Link Prediction
In particular, we elaborately devise a Meta-learning Supported Teacher-student GNN (MST-GNN) that is not only built upon teacher-student architecture for alleviating the migration between "easy" and "hard" samples but also equipped with a meta learning based sample re-weighting module for helping the student GNN distinguish "hard" samples in a fine-grained manner.
Interpreting and Unifying Graph Neural Networks with An Optimization Framework
Graph Neural Networks (GNNs) have received considerable attention on graph-structured data learning for a wide variety of tasks.
AM-GCN: Adaptive Multi-channel Graph Convolutional Networks
We tackle the challenge and propose an adaptive multi-channel graph convolutional networks for semi-supervised classification (AM-GCN).
Structural Deep Clustering Network
The strength of deep clustering methods is to extract the useful representations from the data itself, rather than the structure of data, which receives scarce attention in representation learning.
