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洞見 - Directed Graph Learning Paradigm
Homophily and Heterophily in Semi-supervised Node Classification: AMUD and ADPA
Developing a powerful GNN model that can ensure performance under both homophily and heterophily is crucial for efficient graph learning.
Homophily and Heterophily in Semi-supervised Node Classification: AMUD and ADPA
Developing a powerful GNN model that can ensure performance under both homophily and heterophily is crucial for efficient graph learning.
