Enhancing Graph Neural Networks for Heterophilic Graphs: McGill University Researchers Introduce Directional Graph Attention Networks (DGAT)

Graph neural networks (GNNs) have revolutionized how researchers analyze and learn from data structured in complex networks. These models capture the intricate relationships inherent in graphs, which are omnipresent in social networks, molecular structures, and communication networks, to name a few areas. Central to their success is the ability to effectively process and learn from […]

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