Structural causal model

In recent years, the growing demand for modeling complex systems has brought heterogeneous graph neural networks (HGNNs) into the spotlight. However, existing methods often suffer from fixed inference processes and spurious correlations, limiting their interpretability and generalization ability. To address these challenges, this study proposes a novel heterogeneous graph learning framework that simulates human perception and decision-making processes, enhancing both predictive performance and interpretability.

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