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Class-Balanced and Reinforced Active Learning on Graphs
It learns an optimal policy to acquire class-balanced and informative nodes for annotation, maximizing the performance of GNNs trained with selected labeled nodes.
Self-supervised Heterogeneous Graph Variational Autoencoders
Instead of directly reconstructing raw features for attributed nodes, SHAVA generates the initial low-dimensional representation matrix for all the nodes, based on which raw features of attributed nodes are further reconstructed to leverage accurate attributes.
Prompt Tuning for Multi-View Graph Contrastive Learning
Third, we design a prompting tuning method for our multi-view graph contrastive learning method to bridge the gap between pretexts and downsteam tasks.
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