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New York University Shanghai
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A single model can effectively learn from heterogeneous attributed graphs, outperforming specialized approaches in critical graph learning tasks.
Force GNNs to explicitly reason about graph-level concepts and you get state-of-the-art performance in both classification and interpretability.
