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Toward Robust Graph Semi-Supervised Learning Against Extreme Data Scarcity
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DOI：10.1109/TNNLS.2024.3351938.png)
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The success of graph neural networks (GNNs) in graph-based web mining highly relies on abundant human-annotated data, which is laborious to obtain in practice. When only a few labeled nodes are available, how to improve their robustness is key to achieving replicable and sustainable graph semi-supervised learning. Though self-training is powerful for semi-supervised learning, its application on graph-structured data may fail because 1) larger receptive fields are not leveraged to capture long-range node interactions, which exacerbates the difficulty of propagating feature-label patterns from labeled nodes to unlabeled nodes and 2) limited labeled data makes it challenging to learn well-separated decision boundaries for different node classes without explicitly capturing the underlying semantic structure. To address the challenges of capturing informative structural and semantic knowledge, we propose a new graph data augmentation framework, augmented graph self-training (AGST), which is built with two new (i.e., structural and semantic) augmentation modules on top of a decoupled GST backbone. In this work, we investigate whether this novel framework can learn a robust graph predictive model under the low-data context. We conduct comprehensive evaluations on semi-supervised node classification under different scenarios of limited labeled-node data. The experimental results demonstrate the unique contributions of the novel data augmentation framework for node classification with few labeled data.
Keyword:
Semantics
Data models
Data augmentation
Graph neural networks
Training
Symmetric matrices
Semisupervised learning
Data scarcity
graph neural networks (GNNs)
robustness
self-training
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8.9
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7.6K
被引数:
7.2W
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