MCL improves heterogeneous-graph recommendation accuracy and robustness by combining random masking and random propagation with cross-view contrastive learning on one-hop and meta-path neighborhoods.
A survey of heterogeneous information network analysis,
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Heterogeneous Graph Masked Contrastive Learning for Robust Recommendation
MCL improves heterogeneous-graph recommendation accuracy and robustness by combining random masking and random propagation with cross-view contrastive learning on one-hop and meta-path neighborhoods.