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Node Embedding for Homophilous Graphs with ARGEW: Augmentation of Random walks by Graph Edge Weights
In this paper, we propose ARGEW (Augmentation of Random walks by Graph Edge Weights), a novel augmentation method for random walks that expands the corpus in such a way that nodes with larger edge weights end up with closer embeddings.
LST: Lexicon-Guided Self-Training for Few-Shot Text Classification
Self-training provides an effective means of using an extremely small amount of labeled data to create pseudo-labels for unlabeled data.
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