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Multi-domain graph pre-training suffers from significant data redundancy; MDGMIX leverages boundary-aware subgraph mixing to achieve superior few-shot performance with improved efficiency.
AdaMuS overcomes the bias towards high-dimensional data in multi-view learning by adaptively pruning redundant parameters and sparsely fusing views, leading to improved performance on dimensionally unbalanced data.
