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Found 6 papers, 2 papers with code
Stabilized Self-training with Negative Sampling on Few-labeled Graph Data
Therefore, we propose an effective framework, Stabilized self-training with Negative sampling (SN), which is applicable to existing GNNs to stabilize the training process and enhance the training data, and consequently, boost classification accuracy on graphs with few labeled data.
Decentralized Accelerated Proximal Gradient Descent
In this paper, we propose a new method which establishes the optimal computational complexity and a near optimal communication complexity.
SCE: Scalable Network Embedding from Sparsest Cut
A key of success to such contrastive learning methods is how to draw positive and negative samples.
Multi-consensus Decentralized Accelerated Gradient Descent
This paper considers the decentralized convex optimization problem, which has a wide range of applications in large-scale machine learning, sensor networks, and control theory.
Effective Stabilized Self-Training on Few-Labeled Graph Data
However, under extreme cases when very few labels are available (e. g., 1 labeled node per class), GNNs suffer from severe performance degradation.
Few-shot Classification on Graphs with Structural Regularized GCNs
We consider the fundamental problem of semi-supervised node classification in attributed graphs with a focus on \emph{few-shot} learning.
